Depth from focus (DFF) is a technique for estimating the depth and three-dimensional (3D) shape of an object from a multi-focus image sequence. At present, focus evaluation algorithms based on DFF technology will always cause inaccuracies in deep map recovery from image focus. There are two main reasons behind this issue. The first is that the window size of the focus evaluation operator has been fixed. Therefore, for some pixels, enough neighbor information cannot be covered in a fixed window and is easily disturbed by noise, which results in distortion of the model. For other pixels, the fixed window is too large, which increases the computational burden. The second is the level of difficulty to get the full focus pixels, even though the focus evaluation calculation in the actual calculation process has been completed. In order to overcome these problems, an adaptive window iteration algorithm is proposed to enhance image focus for accurate depth estimation. This algorithm will automatically adjust the window size based on gray differences in a window that aims to solve the fixed window problem. Besides that, it will also iterate evaluation values to enhance the focus evaluation of each pixel. Comparative analysis of the evaluation indicators and model quality has shown the effectiveness of the proposed adaptive window iteration algorithm.

We proposed a three-dimensional (3D) image authentication method using binarized phase images in double random phase integral imaging (InI). Two-dimensional (2D) element images obtained from InI are encoded using a double random phase encryption (DRPE) algorithm. Only part of the phase information is used in the proposed method rather than using all of the amplitude and phase information, which can make the final data sparse and beneficial to data compression, storage, and transmission. Experimental results verified the method and successfully proved the developed 3D authentication process using a nonlinear cross correlation method.

Vascular Doppler optical coherence tomography (DOCT) images with weak boundaries are usually difficult for most algorithms to segment. We propose a modified random walk (MRW) algorithm with a novel regularization for the segmentation of DOCT vessel images. Based on MRW, we perform automatic boundary detection of the vascular wall from intensity images and boundary extraction of the blood flowing region from Doppler phase images. Dice, sensitivity, and specificity coefficients were adopted to verify the segmentation performance. The experimental study on DOCT images of the mouse femoral artery showed the effectiveness of our proposed method, yielding three-dimensional visualization and quantitative evaluation of the vessel.

By improving the long-term correlation tracking (LCT) algorithm, an effective object tracking method, improved LCT (ILCT), is proposed to address the issue of occlusion. If the object is judged being occluded by the designed criterion, which is based on the characteristic of response value curve, an added re-detector will perform re-detection, and the tracker is ordered to stop. Besides, a filtering and adoption strategy of re-detection results is given to choose the most reliable one for the re-initialization of the tracker. Extensive experiments are carried out under the conditions of occlusion, and the results demonstrate that ILCT outperforms some state-of-the-art methods in terms of accuracy and robustness.

In this review, the principle and the optical methods for light-field display are introduced. The light-field display is divided into three categories, including the layer-based method, projector-based method, and integral imaging method. The principle, characteristic, history, and advanced research results of each method are also reviewed. The advantages of light-field display are discussed by comparing it with other display technologies including binocular stereoscopic display, volumetric three-dimensional display, and holographic display.

We propose a novel on-line beam diagnostic method based on single-shot beam splitting phase retrieval. The incident beam to be measured is diffracted into many replicas by a Dammann grating and then propagates through a weakly scattering phase plate with a known structure; the exiting beams propagate along their original direction and form an array of diffraction patterns on the detector plane. By applying the intensity of diffraction patterns into an iterative algorithm and calculating between the grating plane, weakly scattering plane, and detector plane, the complex field of the incident beam can be reconstructed rapidly; the feasibility of this method is verified experimentally with wavelengths of 1053 and 632.8 nm.

A distortion correction method for the elemental images of integral imaging (II) by utilizing the directional diffuser is demonstrated. In the traditional II, the distortion originating from lens aberration wraps elemental images and degrades the image quality severely. According to the theoretical analysis and experiments, it can be proved that the farther the three-dimensional image is displayed from the lens array, the more serious the distortion is. To analyze the process of eliminating lens distortion, one lens and its corresponding elemental image are separated from the traditional II. By introducing the directional diffuser, the aperture stop of the separated optical system changes from the eye’s pupil to the lens. In terms of contrast experiments, the distortion of the improved display system is corrected effectively. In the experiment, when the distance between the reconstructed image and lens array is equal to 120 mm, the largest lens distortion is decreased from 46.6% to 3.3%.

We give a brief overview on the more than 50 years of development of the moment-based image description, the moment invariants, and the orthogonal moments. Some basic ideas for significantly improving the performance of the image moment-based methods, such as the use of the low-order radial moments for reducing information suppression drawback and the separation of the radial basis from the circular harmonic basis for a free selection of the orthogonal radial polynomials, are presented. Performance measures for the orthogonal moments are discussed from the point of view of image analysis. A moment family list is proposed, which includes most of the representative moments in use and the discrete orthogonal moments.

A derivative method for phase retrieval in two-step phase-shifting interferometry (PSI) with a blind phase shift is proposed in this Letter. By numerically calculating the first-order and second-order derivatives of two PSI images, one can directly determine the phase shift and then obtain the quantitative phase image. We illustrate the method with both a theoretical analysis and some simulations of an average-sized red blood cell in different interferometry recording modes. From the results, the validity, accuracy, as well as efficiency of this method are verified. Moreover, this method can be applied to any quantitative phase imaging, including on-axis, off-axis, and slight off-axis interferometry.

Phase diversity (PD) is a kind of wavefront sensing technology based on image collecting and post-processing. We apply the PD technology to align an off-axis three-mirror reflecting anastigmatic system precisely. It can be concluded that the wavefront error obtained by PD agrees well with the interferometric result. The focused images are also restored according to the testing results of PD, and the qualities of restored images are improved.

We present a method to extract foreground object regions efficiently from image sequences. Scale-invariant feature transform algorithm is adopted to estimate the descriptor firstly by matching between two consecutive frames. Given local descriptor matching results, dense motion vector of each pixel is calculated by large displacement optical flow with variational optimization, which integrates detailed descriptors into the variational model. Then the foreground object boundaries and regions are detected by computing the optical flow gradient and magnitude. Experiments demonstrate that the method can achieve better segmentation results than alternative methods and adapts well to moving objects in relatively stationary background image sequences.

We present a simple method for the subsurface imaging of a nucleated cell, which is realized by measuring the difference in wrapped phase between a nucleated cell and its enucleated cell model. The latter one called as the reference phase can be simulated according to the axial thickness and the cytoplasmic refractive index. We illustrate the proposed method with theoretical analysis and numerical simulation of a binucleated cell, and prove its validity on real biological cells by imaging the HeLa cell based on its experimental phase. It shows that this method is suitable for imaging of relatively simple nucleated cells.

We present a tabletop-scale spotlight-mode down-looking synthetic aperture imaging ladar (DL SAIL) demonstrator, which is performed by a collimator with 10 m focal length to simulate the far-field optical field. A specular-point target and a diffuse-reflection target have been used for resolution analysis and 2D imaging, respectively. The experimental result is in agreement with the theoretical design. The experiment setup is capable of simulating a real application scenario for further study. This Letter is focused on the proposition and implementation of spotlight-mode DL SAIL.

In order to improve the reconstruction accuracy in fluorescence molecular tomography (FMT), a common approach is to increase the number of fluorescence data or projections. However, this approach consumes too much memory space and computational time. In this Letter, a data compression strategy that involves the removal of the redundant information from both intra- and inter-projections is proposed to reduce the dimension of the FMT inverse problem. The performance of this strategy is tested with phantom and in vivo mouse experiments. The results demonstrate that the proposed data compression strategy can accelerate the FMT reconstruction nearly tenfold and almost without any quality degradation.

The periodic ripple structures on wolfram and titanium surfaces are induced experimentally by linear polarized femtosecond laser pulses at small incident angles. The structural features show a material difference in the s- and p-polarized laser irradiation. The interspace between the ripples increases significantly for p-polarized laser irradiation when it exceeds a threshold angle, and the ripples’ periodicities are larger than the wavelength of the incident p-polarized femtosecond laser; however, no significant change in the period of the ripples is observed with increasing incident angle for s-polarized laser irradiation. To explain these phenomena we propose a resonant absorption mechanism, by which the experimental observations can be interpreted.

The energy of light exposed on human skin is compulsively limited for safety reasons which affects the power of photoacoustic (PA) signal and its signal-to-noise ratio (SNR) level. Thus, the final reconstructed PA image quality is degraded. This Letter proposes an adaptive multi-sample-based approach to enhance the SNR of PA signals and in addition, detailed information in rebuilt PA images that used to be buried in the noise can be distinguished. Both ex vivo and in vivo experiments are conducted to validate the effectiveness of our proposed method which provides its potential value in clinical trials.

The average bit-error rate (ABER) performance of free-space optical (FSO) communication links is investigated for space-shift keying (SSK) over log-normal and negative-exponential atmospheric turbulence channels. SSK is compared with repetition codes and a single-input single-output system using multiple pulse amplitude modulations. Simulation results show that the signal-to-noise ratio gain of SSK largely increases with greater spectral efficiencies and/or higher turbulence effects. A tight bound for ABER is derived based on an exact moment generation function (MGF) for negative-exponential channel and an approximate MGF for log-normal channel. Finally, extensive Monte Carlo simulations are run to validate the analytical analysis.

A transfer method is introduced to derive the normalized radiance for CE318 Sun/sky radiometer using viewing solid angles and extraterrestrial calibration constants. The new transfer method has a good consistency at different parts of the sky scanning. Error analysis suggests that the uncertainty of the transferred method is about 2.0%–2.4%. The normalized radiances are used as input of the aerosol inversion to test the performance of the new transfer method. The residuals of the inversion (e.g., difference between fitted and measured radiance) are chosen as the index of the radiance calibration accuracy. Analyses of one year’s measurements in Beijing suggest an average sky residual of 3.3% for almucantar scanning (while 3.7% for the AERONET method), which suggest a better accuracy of the transfer method when used in aerosol retrieval.

Laser-driven ultrafast X-ray sources are widely used for diagnostic radiography. However, there is a large divergence of fast electrons when they are generated by an intense short-pulse laser interacting with a foil target. We design a nanowire array target to achieve a more compact point X-ray source. Fast electrons are confined and guided by the nanowire array structure in order to generate a Kα source with a small spot size. In our work, the smallest measured source size is comparable to the laser spot size, while the conversion efficiency can reach 2.4×10 4.

We present a high dense views auto-stereoscopic three-dimensional display method with the projectors array and lenticular lens array (LLA) screen. The principle and configuration are demonstrated. This display method utilizes lenticular screen to modulate the information of projectors. To increase the dense of views, we propose a novel arrangement way of projectors array. In the experiment, the viewer can obtain smooth motion parallax and evident stereo feeling at optimal distance. Through analyzing and observation, the maximum display depth is found to be more than 50 cm.

In this letter, we propose a novel adaptive image enhancement algorithm based on nonsubsampled contourlet transform (NSCT) coefficient histogram matching. Firstly, the original image is decomposed in the NSCT domain. Secondly, the NSCT coefficient histograms of the original image in corresponding subbands are adaptively mapped to those of the reference image via histogram matching after threshold denoising. Finally, the enhanced image is reconstructed from the modified coefficients via inverse NSCT. Experimental results demonstrate that the proposed adaptive algorithm effectively improves subtle features while suppressing noise compared with existing algorithms.

Hyperspectral optimization process exemplar, a&bb-based K model, and a water column correction model are used to process the hyperspectral data for detecting the subtle spectral difference of coral reefs. The water column correction model only tracks those effective photons by fully considering the geometrical distribution of the light field. The adaptivity of the parameters and models to the in situ data collected in Sanya Bay is evaluated. The modeled and uncorrected spectra are examined separately to reflect the coral reflectance, and the coefficients of determination for the relationships drops from 0.90 to 0.05. The retrieved bottom reflectance for 70 corals (Acropora, Porites) exhibited the classic chlorophyll features. The reflectance at 700 nm collected in Sanya Bay is relatively lower than the results conducted by other researchers. Peak ratio index and derivative analysis are utilized and are proved to be effective for coral reef classification and coral healthy assessment.

We propose modified local mean decomposition to analyze signals of fringe pattern adaptively. It decomposes the signals into a set of functions, each of which is the product of amplitude signal and frequency signal. Then, the physical components of noise and background are extracted according to the corresponding product functions. Moreover, to solve the most likely mode mixing problem, a high-frequency signal is constructed according to the amplitude and frequency characteristics of the intrinsic noise. Using the presented method, carrier signals are recovered accurately as well as the wrapped phase. Compared experiments illustrate the validity of this method.

The application of dark-field (DF) technique with adaptive optics for retinal imaging in vivo is presented to improve retinal imaging contrast. The influence on the imaging contrast introduced by dynamic ocular aberrations should be considered. The qualitative analysis of the influence is discussed. Detail simulation and quantitative analysis are presented. According to our simulation results, when residual aberrations are reduced to less than 5%, it has a little impact on the contrast of DF imaging; while, on increasing the aberrations up to 10%, the contrast of the DF imaging falls off sharply.

Quantitative phase imaging (QPI) of biological cells is an important developing technique. In this paper, a derivative method of phase extraction is put out under several typical blood cells' models, which is completed by numerically simulating the process of QPI. Furthermore, the first-order derivative of the phase is introduced to analyze the cell morphology, and a monocyte model is discussed as an example to demonstrate the method. It shows that the first-order derivative of the phase can be used as a tool for the identification of blood cells.

This letter presents a correlation tracking algorithm based on edge detection, in allusion to the phenomenon that the target is susceptible to interference in long-wave infrared image. Firstly, the collected imageis processed by median filtering to remove the defects in the detector. Then thegradient information of the background is filtered out by edge detection method. Secondly, to enhance the edge information, the image is dealt with mathematical morphology (MM). And finally, the correlation matching method is done to acquire the miss-distance information of the target in the image. Through theoretical simulation and practical verification, it is proved that the algorithm has a better effect on inhibiting most of the background information, protecting the target's information effectively, enlarging the weight of the target's information in the correlation operation and improving stability of the detector.

The resolution of astronomical imaging from large optical telescopes is usually limited by the blurring effects of refractive index fluctuations in the Earth’s atmosphere. In this letter, we develop a lucky imaging system to restore astronomical images through atmosphere turbulence on large telescope. Our system takes very short exposures, on the order of the atmospheric coherence time. The rapidly changing turbulence leads to a very variable point spread function (PSF), and the variability of the PSF leads to some frames having better quality than the rest. Only the best frames are selected, aligned and co-added to give a final image with much improved angular resolution. Our lucky imaging system is successfully applied to restore the astronomical images taken by a 1.23 m telescope. We get clear images of moon surface, Jupiter, and Saturn, and our system can be demonstrated to greatly improve the imaging resolution through atmospheric turbulence.

We establish an accurate and prompt intraoperative sentinel lymph node (SLN) diagnostic system using in laparoscopy for gastric cancer. The system consists of a near infrared (NIR) laser, a near infrared camera, a spectroscope, two optical fibers, a monitor and imaging analyzer. The experiments on animals are performed to assess the availability of this system. It is able to verify fluorescence imaging and spectrum of gastric SLN, after injecting indocyanine green (ICG) (1 ml: 0.5?1 mg/ml) to the subserosa.

A modified method of image denoising based on multi-scale mathematical morphology is proposed to be used in the earthquake ruin scene. A new morphological arithmetic operator is desiged according to the basic operations in the mathematical morphology, then the multi-scale operation filter is used based on new operator to realize image denoising. Compared with the other methods, this new method can reduce noise of earthquake ruin scene effectively and has a good practicability.

A new approach is proposed to extract the rectangular building footprints from single high-resolution synthetic aperture radar (SAR) image by combining region-based information with edge-based information. We build multiscale autoregressive (MAR) model and likelihood rate classifier for SAR image to extract region information. Edge information is extracted from SAR image by using constant false alarm rate edge detector. We propose a new optimization criterion which fuses the previous extracted region information and edge information to obtain the final building footprints. The experimental result shows that the proposed method can extract the building footprints effectively.

Integer wavelet transform (IWT) could offer the lower computational complexity and the less storage spending for image compression than the discrete wavelet transform (DWT). But the most coefficients of the IWT image have smaller dynamic change ranges than discrete wavelet transform. In this letter, an efficient and low-complerity coding algorithm called embedded optimal coefficient scaling (EOCS) is proposed. It optimizes the distribution of the wavelet coefficients in every threshold plane and provides an efficient embedded quadtree-partitioning scheme to encode the image. Experimental results show that the presented method cannot provide peak signal-to-noise ratio (PSNR) performance up about 2–6 dB better than set partitioning in hierarohical trees (SPIHT) without the OCSF scheme, but also support both efficiently lossy and lossless compression in single bitstream.

An effective image splicing algorithm based on phase correlation and speeded-UP robust features (SURF) operator is proposed which can sort the disordered sequence and stitch them into a super viewing field image without any human intervention. Phase correlation in frequency domain is used for images sorting and region ofinterest (ROI) estimation, and guiding features extracting and matching in spatial domain by SURF operator and bidirectional best bin first (BBF) strategy. The experimental results demonstrate that this algorithm not only can deal with the input images with translation, rotation and scale changes, but also outperforms the pre-existing methods on the aspect of repeatability, efficiency and accuracy.

A rational-dilation wavelet transform (RWT) based pan-sharpening method for multi-spectral (MS) images of various oscillatory nature is proposed. The previous multi-scale transforms, such as wavelets, curvelets and contourlets, decompose an image into channels with low constant Q-factors, and aren't suitable for pan-sharpening images with different behavior in frequency domain. The RWT as an over-complete scheme not only increases the sampling in spatial and frequency domain, but also provides a tunable Q-factor approach to be suitable for a given dataset. We studied its multi-scale decomposition scheme and the RWT based pan-sharpening method. The MS image pan-sharpening experiments show that this method using a better suitable parameter set can achieve a promising performance and often outperforms many other widely-used pan-sharpening methods both in visual quality and in term of evaluation indexes.

In order to broaden the scope of application and ensure the calibration precision, a new method of linear calibration by making fully use of vanishing point attributes is proposed. The method dose not need any rigorous restrictions, and solves the self-calibration problem with only five vanishing points in two digital images, which are arbitrarily taken by a handheld digital camera. Furthermore, another approach for camera's pose estimation is also put forward without any strict controlled motions. The experimental results of both computer simulation and real images show that the calibration algorithm is effective, feasible, and robust.

By making use of the discrete fractional cosine transform, spectrum cutting and combining, rate-distortion control and color space transform, a joint compression and encryption scheme for multiple images and color images is proposed based on the multiple-order discrete fractional cosine transform (MODFrCT). The spectra coefficients of images gotten by the discrete cosine transform are scanned in a way of zigzag, cut at an appropriate position, and combined into a single spectrum image sequentially encrypted by the MODFrCT. Rate-distortion control is utilized during the spectrum cutting to balance the qualities of the multiple reconstructed images. A color image can be decomposed into Y, Cb, and Cr components prior to the encryption, and these three components are then encrypted in the same way as that for multiple images. The numerical simulations demonstrate the validity and efficiency of these schemes, and the robustness of the schemes against occlusion attack is examined.

An unsupervised segmentation and its performance evaluation technique are proposed for synthetic aperture radar (SAR) image based on the mixture multiscale autoregressive (MMAR) model and the bootstrap method. The segmentation-evaluation techniques consist of detecting the number of image regains, estimating MMAR parameters by using bootstrap stochastic annealing expectation-maximization (BSAEM) algorithm, and classifying pixels into region by using Bayesian classifier. Experimental results demonstrate that the evaluation operation is robust, and the proposed segmentation method is superior to the traditional single resolution techniques, and considerably reduces the computing time over the EM algorithm.

Oil microleakage can cause the land surface vegetation to be abnormal. An oil and gas exploration method based on the vegetation information in the hyperspectral remote sensing images is proposed. It's used to probe the effectiveness of extracting the oil and gas microleakage information, with the vegetation anomalies in the remote sensing images. A decision tree based on the vegetation index is taken to extract the anomalies areas of vegetation in the CASI images. It's shown by the experiment that there are some potential for the exploration of oil and gas in the areas covered by sparse vegetations.

For many years, various local feature descriptors have been proposed. Among them, Lowe's scale invariant feature transform (SIFT) descriptor is the most successful one and has been proven to be performed better n the distinctiveness and robustness than other descriptors. However, SIFT descriptor is based on gray level images and pays little attention to the color information which can be a powerful cue in the distinction and recognition of objects. To increase the discriminative power, color features have been plugged into the feature descriptors only recently. In this letter, we study the photometric invariant properties of the Lowe's SIFT, HueSIFT, rgSIFT and CSIFT based on color diagonal offset model. Theoretical and experimental results show that the four descriptors are not fully invariant to photometric transformation. To solve this problem, a new color invariant framework based on color diagonal offset model is proposed in this letter. Experimental results validate our proposed framework.

We illustrate an approach to statistical model and sequential hypothesis designed to the automatic target recognition (ATR) problem for active imaging LADAR. The key to this approach is using multihypothesis sequential tests to reduce the number of target hypotheses under consideration as more observed data are processed. The approach is potentially useful when sensor data are plentiful but computation time and processing capability are constrained. We experimentally demonstrate that the proposed recognition approach can resolve the military ground vehicle recognition problem of active imaging LADAR with a high recognition rate.

Image matching is an important question in computer vision, however, due to the large viewpoint and similar regions, there exist false matches. A robust matching method-DelTri is proposed. Based on the initial matching of Scale Invariant Feature Transform, the matched keypoints are respectively triangulated to create the triangulation net, which can express the overlapped physical structure of the objects. The matched triangles can lead to the final matches. Compared with classical RANSAC, experiments show that DelTri can improve the match robustness, including matching accuracy and magnitude efficiency.

The extraction of stable local features directly affects the performance of infrared face recognition algorithms. Recent studies on the application of scale invariant feature transform (SIFT) to infrared face recognition show that star-styled window filter (SWF) can filter out errors incorrectly introduced by SIFT. The current letter proposes an improved filter pattern called Y-styled window filter (YWF) to further eliminate the wrong matches. Compared with SWF, YWF patterns are sparser and do not maintain rotation invariance; thus, they are more suitable to infrared face recognition. Our experimental results demonstrate that a YWF-based averaging window outperforms an SWF-based one in reducing wrong matches, therefore improving the reliability of infrared face recognition systems.

A multilayer fiber bundle is used to couple the image in a remote sensing imaging system. The object image passes through all layers of the fiber bundle in micro-scanning mode. The malposition of adjacent layers arranged in a hexagonal pattern is at sub-pixel scale. Therefore, sub-pixel processing can be applied to improve the spatial resolution. The images coupled by the adjacent layer fibers are separated, and subsequently, the intermediate image is obtained by histogram matching based on one of the separated image called base image. Finally, the intermediate and base images are processed in the frequency domain. The malposition of the adjacent layer fiber is converted to the phase difference in Fourier transform. Considering the limited sensitivity of the experimental instruments and human sight, the image is set as a band-limited signal and the interpolation function of image fusion is found. The results indicate that a super-resolution image with ultra-high spatial resolution is obtained.

A novel hybrid fitting energy-based active contour model in the level set framework is proposed. The method fuses the region and boundary information of the target to achieve accurate and robust detection performance. A special extra term that penalizes the deviation of the level set function from a signed distance function is also included in our method. This term allows the time-consuming redistancing operation to be removed completely. Moreover, a fast unconditionally stable numerical scheme is introduced to solve the problem. Experimental results on real infrared images show that our method can improve target detection performance efficiently in terms of the number of iterations and the wasted central processing unit (CPU) time.

The photoacoustic tomography (PAT) method, based on compressive sensing (CS) theory, requires that, for the CS reconstruction, the desired image should have a sparse representation in a known transform domain. However, the sparsity of photoacoustic signals is destroyed because noises always exist. Therefore, the original sparse signal cannot be effectively recovered using the general reconstruction algorithm. In this study, Bayesian compressive sensing (BCS) is employed to obtain highly sparse representations of photoacoustic images based on a set of noisy CS measurements. Results of simulation demonstrate that the BCS-reconstructed image can achieve superior performance than other state-of-the-art CS-reconstruction algorithms.

A technique to construct an affine invariant descriptor for remote-sensing image registration based on the scale invariant features transform (SIFT) in a kernel space is proposed. Affine invariant SIFT descriptor is first developed in an elliptical region determined by the Hessian matrix of the feature points. Thereafter, the descriptor is mapped to a feature space induced by a kernel, and a new descriptor is constructed by whitening the mapped descriptor in the feature space, with the transform called KW-SIFT. In a final step, the new descriptor is used to register remote-sensing images. Experimental results for remote-sensing image registration indicate that the proposed method improves the registration performance as compared with other related methods.

Stripe nonuniformity is very typical in line infrared focal plane (IRFPA) and uncooled starring IRFPA. We develop the minimum mean square error (MMSE) method for stripe nonuniformity correction (NUC). The goal of the MMSE method is to determine the optimal NUC parameters for making the corrected image the closest to the ideal image. Moreover, this method can be achieved in one frame, making it more competitive than other scene-based NUC algorithms. We also demonstrate the calibration results of our algorithm using real and virtual infrared image sequences. The experiments verify the positive effect of our algorithm.

A hand vein authentication system in which the identity of an individual can be readily confirmed upon gripping a handle is proposed. This recognition method incorporates infrared light-emitting diode (LED) onto a door handle and sets a charge-coupled device (CCD) camera on the other side of the hand. It builds on fuzzy c-means clustering and parallel neural networks (NNs); moreover, it is expected to solve the pattern recognition problem in large-scale databases using NNs due to its self-learning and parallel processing capabilities and by effectively incorporating training patterns. The experimental results validate the e±ciency of the proposed algorithm.

We propose improved multilevel filters (IMLFs) involving the absolute value operation into the algorithmic framework of traditional multilevel filters (MLFs) to improve the robustness of infrared small target enhancement techniques under a complex infrared cluttered background. Compared with the widely used small target enhancement methods which only deal with bright targets, the proposed technique can enhance the infrared small target, whether it is bright or dark. Experimental results verify that the proposed technique is efficient and practical.

A novel indirect building localization technique based on a prominent solid landmark from a forward-looking infrared imagery is proposed to localize low, deeply buried, or carefully camouflaged buildings in dense urban areas. First, the widely used effective methods are applied to detect and localize the solid landmark. The building target is then precisely indirectly localized by perspective transformation according to the imaging parameters and the space constraint relations between the building target and the solid landmark. Experimental results demonstrate this technique can indirectly localize buildings in dense urban areas effectively.

Synthetic aperture integral imaging provides the ability to reconstruct partially occluded objects from multi-view images. However, the reconstructed images suffer from degraded contrast due to the superimposition of foreground defocus blur. We propose an algorithm to remove foreground occlusions before reconstructing backgrounds. Occlusions are identified by estimating the color variance on elemental images and then deleting it in the final synthetic image. We demonstrate the superiority of our method by presenting experimental results as well as comparing our method with other approaches.

Shearlets not only possess all properties that other transforms have, but also are equipped with a rich mathematical structure similar to wavelets, which are associated to a multi-resolution analysis. Recently, shearlets have been used in image denoising, sparse image representation, and edge detection. However, its application in image fusion is still under study. In this letter, we study the feasibility of image fusion using shearlets. Fusion rules of larger high-frequency coefficients based on regional energy, regional variance, and absolute value are proposed because shearlet transform can catch detailed information in any scale and any direction. The fusion accuracy is also further improved by a region consistency check. Several different experiments are adopted to prove that fusion results based on shearlet transform can acquire better fusion quality than any other method.

To apply decision level fusion to hyperspectral remote sensing (HRS) image classification, three decision level fusion strategies are experimented on and compared, namely, linear consensus algorithm, improved evidence theory, and the proposed support vector machine (SVM) combiner. To evaluate the effects of the input features on classification performance, four schemes are used to organize input features for member classifiers. In the experiment, by using the operational modular imaging spectrometer (OMIS) II HRS image, the decision level fusion is shown as an effective way for improving the classification accuracy of the HRS image, and the proposed SVM combiner is especially suitable for decision level fusion. The results also indicate that the optimization of input features can improve the classification performance.

We present a spiral phase filtering system with a large tolerance for edge enhancement of both phase and amplitude objects in optical microscopy. The method is based on a Fourier 4-f spatial filtering system. A phase mismatched spiral phase plate (SPP) fabricated by electron beam lithography is employed as the radial Hilbert transform for image edge enhancement. Compared with holography, SPP is simple, economical, reliable, and easy to integrate.

The operational procedures for efficiently reconstructing the two-dimensional image of a body by the filtered back projection are described in this paper. The projections are interpolated for four times of original projection by zero-padding the original projection in frequency-domain and then inverse fast Fourier transform (FFT) is taken to improve accuracy. Nearest interpolation is applied to decrease operation time. Projection dependence of next pixel at the same row is used. For each row of image, the first pixel projection once for each angle is pre-computed, other pixel projection of this row can be found by iteration. Therefore, the pre-interpolation process only has to be performed once for each row, rather than individually for each pixel. It greatly reduces the amount of computation. Compared with original implementation, the speed of reconstruction image is nearly improved by five times. The image accuracy is still preserved.