Blind image deconvolution is one method of restoring both kernel and real sharp image only from de-graded images, due to its illness, image priors are necessarily applied to constrain the solution. Given the fact that traditional image gradient l2 and l1 norm priors cannot describe the gradient distribution of natural images, in this paper, the image sparse prior is applied to the restoration of single-frame atmospheric turbulence degraded images. Kernel estimation is performed first, followed by non-blind restoration and the split Bregman algorithm is used to solve the non-convex cost function. Simulation results show that compared with total variation priori, sparse priori is better at kernel estimation, producing sharp edges and removal of ringing, etc., which reducing the kernel estimation error and improving restoration quality. Finally, the real turbulence-degraded images are restored.

A preternatural and extremely thin metasurface with weak asymmetric unit structure is presented here to demonstrate extraordinary strong chirality. The unit cell of metasurface is composed of a double layer of elliptical metal patches with a certain twisted angle and a medium sandwiched between them. When the twisted angle equals to 80°, optical activity can be realized in this metasurface. At the resonant frequency 11.89 GHz, the incident linearly polarized wave is converted into its cross-polarization wave with the transmittance rate higher than 94%. The light weight and miniaturization of this metasurface provide a reliable approach for polarization manipulation. If extended to light waveband, the metasurface may have potentials in biological applications such as detection of weak chiral molecules, etc.

At present, several mainstream algorithms using color name (CN) all adopt principal component analysis (PCA) to process the feature. However, PCA assumes that the noise of input data must obey Gaussian distribution, which is a conspicuous defect. Aim to address this problem, in this paper, we take robust principal component analysis (Robust PCA) to process CN features. The method projects the original RGB color space to a robust color space–CN space, which means that the input image is stratified to 11 layers according to color name. Then, it processes the CN features by the Robust PCA, so that the mapped image information is concentrated on a few layers, retaining a great quantity of image information and filting out noise. The processed feature is used for Color-tracking frame at the standard benchmark OTB100, and we set up different layers to compare the performance differences of the algorithm. The experimental results show that the success rate increases by 1.0% and the accu-racy increases by 0.9% at OTB100. The result illustrates that the Robust PCA method can better bring color name feature superiority into full play and improve the performance of the algorithm effectively.

A 3D projection system based on complementary multiband bandpass filter (CMBF) is proposed in this paper, which enables viewers to gain 3D experience through special glasses. Different from the time-multiplex or the spatial-multiplex system, it is a spectrum-multiplex system using pairs of CMBFs. The three pairs of complementary bandpass of a pair of CMBFs can be designed to cover the three spectrum ranges of RGB individually and in each pair the two bandpass nearly do not overlap. In this paper, a 3D projection system is built from two ordinary projec-tors and its spectrum, brightness and crosstalk have been measured. The average crosstalk is 3%, meeting the ba-sic requirement of crosstalk in 3D display which is less than 10%.

The observation and recognition of sunspots is an important task of solar physics. By observing and analyzing sunspots, solar physicists are able to analyze and predict solar activities with higher accuracy. With the continuous progress of observation instruments, solar full-disk image data amount is also on a rapid growth. In order to recognize and label sunspots quickly and accurately, a two-layer sunspot recognition model is proposed in this paper. The first layer model is based on deep learning model YOLO. In order to enhance the ability of YOLO to recognize small sunspots, the parameters of YOLO are optimized by using the k-means algorithm based on inter-section-over-union. The final YOLO model can identify most large sunspots and sunspot groups, with only a few isolated small sunspots being unidentified. For the purpose of further improving recognition rate of small sunspots, the second layer model applies AGAST (adaptive and generic accelerated segment test) feature detection algorithm to specifically identify the missing small sunspots. The experimental results on SDO/HMI sunspot data set show that all kinds of sunspots can be recognized effectively with high recognition accuracy by using the model proposed in this paper, thus realizing the real-time sunspot detection task.

With the problem of difficulty that a single filter to adapt to various complex changes in the tracking process, an adaptive multi-filter target tracking algorithm based on the efficient convolution operators for tracking is proposed. Spatial-temporal regularized filter, the consistency check filter and the correlation filter in the efficient convolution operator tracker, convolve with target features respectively, which obtains three detection scores. The training method of spatial-temporal regularized filter is to introduce temporal regularization into loss function. The consistency check filter is a filter that uses current filter to track the target of previous several frames and updates only when the error of forward and backward position is less than the threshold. Target position is estimated by the best filter detection score with the peak-to-side ratio is maximum. The improved algorithm is tested with the OTB-2015 dataset and UAV123 dataset. The experimental results show that the proposed algorithm can better adapt to the complex environment in tracking process, which has high precision and robustness.

To solve the unevenness of distributions of optical illuminance and power in visible light communication system, a light source layout based on multi-population genetic algorithm is proposed. Taking 15 LED lamps as an example, the position coordinates were optimized under the fitness function related to variance of received power through the co-evolution of multi-populations. The simulation results on Matlab R2016a showed that, after being op-timized, the distribution of power was evener intuitively, the variance of power reached 1.5744 dBm, the illuminance fell in a range between 889 lx and 1009 lx and the uniformity ratio of illuminance was 91.73%, all of which were better than those of the layout optimized by traditional genetic algorithm and the rectangular layout optimized by mul-ti-population genetic algorithm. This experiment provides a feasible solution for optimizing the visible light commu-nication system so that users can have a more comfortable communication trip in this system.

Side-scan sonar (SSS) is an electronic device that utilizes the propagation characteristics of sound waves under water to complete underwater detection. Because the SSS produces images and maps according to the in-tensity of acoustic echo, speckle noise will be inevitably involved. A speckle denoising method based on block-matching and 3D filtering (BM3D) is proposed to filter the multiplicative speckle noise in SSS images. First, the SSS image is transformed by power and logarithm. The wavelet transform is used to estimate the general noisy level of the polluted image. Second, the parameters of the BM3D algorithm are updated according to the noise estimation results of each local patch. At last, after comparing the general noise estimation and the local noise estimation, the proposed algorithm chooses the best estimation to filter every patch separately to solve the problem that the noise is not evenly distributed. The experimental results show that the improved BM3D algorithm can effectively reduce the speckle noise in SSS images and obtain good visual effects. The Equivalent Number of Looks of the proposed al-gorithm is at least 6.83% higher, the Speckle Suppression Index is lower than traditional algorithm, and the Speckle Suppression and Mean Preservation Index is reduced by at least 3.30%. This method is mainly used for sonar image noise reduction, and has certain practical values for ultrasonic, radar or OCT images polluted by speckle noise.