Journal of Applied Optics, Volume. 45, Issue 6, 1095(2024)

Review of low-illuminance image enhancement algorithm based on deep learning

Ziwei LI1... Jinlong LIU1,*, Huizhen YANG2 and Zhiguang ZHANG1 |Show fewer author(s)
Author Affiliations
  • 1School of Electronic Engineering, Jiangsu Ocean University, Lianyungang 222005, China
  • 2School of Network and Communication Engineering, Jinling Institute of Technology, Nanjing 211169, China
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    References(62)

    [1] D YIN, W TANG, P CHEN et al. An improved algorithm for target detection in low light conditions. Journal of Physics: Conference Series, 2203, 012045(2022).

    [2] W WANG, X YUAN, X WU et al. Fast image dehazing method based on linear transformation. IEEE Transactions on Multimedia, 19, 1142-1155(2017).

    [3] W KIM, S SUH, W HWANG et al. SVD face: illumination-invariant face representation. IEEE Signal Processing Letters, 21, 1336-1340(2014).

    [4] W WANG, X WU, X YUAN et al. An experiment-based review of low-light image enhancement methods. IEEE Access, 8, 87884-87917(2020).

    [5] R C GONZALEZ. Digital image processing(2009).

    [6] X FU, D ZENG, Y HUANG et al. A fusion-based enhancing method for weakly illuminated images. Signal Processing, 129, 82-96(2016).

    [7] E H LAND, J J MCCANN. Lightness and retinex theory. JOSA, 61, 1-11(1971).

    [8] H D CHENG, X J SHI. A simple and effective histogram equalization approach to image enhancement. Digital signal processing, 14, 158-170(2004).

    [9] C JUNG, Q YANG, T SUN et al. Low light image enhancement with dual-tree complex wavelet transform. Journal of Visual Communication and Image Representation, 42, 28-36(2017).

    [10] C WANG, H WU, Z JIN. FourLLIE: boosting low-light image enhancement by fourier frequency information.

    [11] X YANG, X JIANG, J DU. Low illumination image enhancement algorithm based on gamma transformation and fractional order. Computational Engineering Design, 42, 762-769(2021).

    [12] S WANG, J ZHENG, H M HU et al. Naturalness preserved enhancement algorithm for non-uniform illumination images. IEEE Transactions on Image Processing, 22, 3538-3548(2013).

    [13] C LI, C GUO, L HAN et al. Low-light image and video enhancement using deep learning: a survey. IEEE Transactions on Pattern Analysis and Machine Intelligence, 44, 9396-9416(2021).

    [14] K G LORE, A AKINTAYO, S SARKAR. LLNet: a deep autoencoder approach to natural low-light image enhancement. Pattern Recognition, 61, 650-662(2017).

    [15] Z FU, Y YANG, X TU et al. Learning a simple low-light image enhancer from paired low-light instances, 22252-22261(2023).

    [16] F LV, B LIU, F LU. Fast enhancement for non-uniform illumination images using light-weight CNNs, 1450-1458(2020).

    [17] C LI, C GUO, C C LOY. Learning to enhance low-light image via zero-reference deep curve estimation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 44, 4225-4238(2021).

    [18] V JAIN, S SEUNG. Natural image denoising with convolutional networks. Advances in Neural Information Processing Systems, 21, 1-8(2008).

    [19] F LV, F LU, J WU et al. MBLLEN: low-light image/video enhancement using CNNs. BMVC, 220, 1-4(2018).

    [20] C CHEN, Q CHEN, J XU et al. Learning to see in the dark, 3291-3300(2018).

    [21] J LI, J LI, F FANG et al. Luminance-aware pyramid network for low-light image enhancement. IEEE Transactions on Multimedia, 23, 3153-3165(2020).

    [22] S LIM, W KIM. DSLR: deep stacked Laplacian restorer for low-light image enhancement. IEEE Transactions on Multimedia, 23, 4272-4284(2020).

    [23] R WANG, Q ZHANG, C W FU et al. Underexposed photo enhancement using deep illumination estimation, 6849-6857(2019).

    [24] L W WANG, Z S LIU, W C SIU et al. Lightening network for low-light image enhancement. IEEE Transactions on Image Processing, 29, 7984-7996(2020).

    [25] A ZOTIN. Fast algorithm of image enhancement based on multi-scale retinex. Procedia Computer Science, 131, 6-14(2018).

    [26] C WEI, W WANG, W YANG et al. Deep retinex decomposition for low-light enhancement.

    [27] C LI, J GUO, F PORIKLI et al. LightenNet: a convolutional neural network for weakly illuminated image enhancement. Pattern Recognition Letters, 104, 15-22(2018).

    [28] Y ZHANG, J ZHANG, X GUO. Kindling the darkness: a practical low-light image enhancer, 1632-1640(2019).

    [29] Y ZHANG, X GUO, J MA et al. Beyond brightening low-light images. International Journal of Computer Vision, 129, 1013-1037(2021).

    [30] Y JIANG, X GONG, D LIU et al. EnlightenGAN: deep light enhancement without paired supervision. IEEE Transactions on Image Processing, 30, 2340-2349(2021).

    [31] O RONNEBERGER, P FISCHER, T BROX. U-net: convolutional networks for biomedical image segmentation, 234-241(2015).

    [32] W XIONG, D LIU, X SHEN et al. Unsupervised low-light image enhancement with decoupled networks, 457-463(2022).

    [33] J HU, X GUO, J CHEN et al. A two-stage unsupervised approach for low light image enhancement. IEEE Robotics and Automation Letters, 6, 8363-8370(2021).

    [34] L MA, T MA, R LIU et al. Toward fast, flexible, and robust low-light image enhancement, 5637-5646(2022).

    [35] W YANG, S WANG, Y FANG et al. From fidelity to perceptual quality: A semi-supervised approach for low-light image enhancement, 3063-3072(2020).

    [36] W KOZLOWSKI, M SZACHNIEWICZ, M STYPULKOWSKI et al. Dimma: semi-supervised low light image enhancement with adaptive dimming.

    [37] L ZHANG, L ZHANG, X LIU et al. Zero-shot restoration of back-lit images using deep internal learning, 1623-1631(2019).

    [38] A ZHU, L ZHANG, Y SHEN et al. Zero-shot restoration of underexposed images via robust retinex decomposition, 1-6(2020).

    [39] C GUO, C LI, J GUO et al. Zero-reference deep curve estimation for low-light image enhancement, 1780-1789(2020).

    [40] J CAI, S GU, L ZHANG. Learning a deep single image contrast enhancer from multi-exposure images. IEEE Transactions on Image Processing, 27, 2049-2062(2018).

    [41] C CHEN, Q CHEN, M N DO et al. Seeing motion in the dark, 3185-3194(2019).

    [42] H JIANG, Y ZHENG. Learning to see moving objects in the dark, 7324-7333(2019).

    [43] X XU, R WANG, C W FU et al. SNR-aware low-light image enhancement, 17714-17724(2022).

    [44] K LU, L ZHANG. TBEFN: a two-branch exposure-fusion network for low-light image enhancement. IEEE Transactions on Multimedia, 23, 4093-4105(2020).

    [45] Z ZHAO, B XIONG, L WANG et al. RetinexDIP: a unified deep framework for low-light image enhancement. IEEE Transactions on Circuits and Systems for Video Technology, 32, 1076-1088(2021).

    [46] R LIU, L MA, J ZHANG et al. Retinex-inspired unrolling with cooperative prior architecture search for low-light image enhancement, 10561-10570(2021).

    [47] X GUO, Y LI, H LING. LIME: Low-light image enhancement via illumination map estimation. IEEE Transactions on Image Processing, 26, 982-993(2016).

    [48] C LEE, C LEE, C S KIM. Contrast enhancement based on layered difference representation of 2D histograms. IEEE Transactions on Image Processing, 22, 5372-5384(2013).

    [49] Y P LOH, C S CHAN. Getting to know low-light images with the exclusively dark dataset. Computer Vision and Image Understanding, 178, 30-42(2019).

    [50] J LIU, D XU, W YANG et al. Benchmarking low-light image enhancement and beyond. International Journal of Computer Vision, 129, 1153-1184(2021).

    [51] V BYCHKOVSKY, S PARIS, E CHAN et al. Learning photographic global tonal adjustment with a database of input/output image pairs, 97-104(2011).

    [52] T WANG, K ZHANG, T SHEN et al. Ultra-high-definition low-light image enhancement: a benchmark and transformer-based method. Proceedings of the AAAI Conference on Artificial Intelligence, 37, 2654-2662(2023).

    [53] H ZHAO, O GALLO, I FROSIO et al. Loss functions for image restoration with neural networks. IEEE Transactions on Computational Imaging, 3, 47-57(2016).

    [54] C LEDIG, L THEIS, F HUSZAR et al. Photo-realistic single image super-resolution using a generative adversarial network, 4681-4690(2017).

    [55] X WANG, K YU, S WU et al. Esrgan: enhanced super-resolution generative adversarial networks, 63-79(2018).

    [56] M BONTONOU, K R E C LASSANCE, B G HACENE et al. Introducing graph smoothness loss for training deep learning architectures.

    [57] L BING, Q HAINA, X WEIHUA et al. Ranking-based color constancy with limited training samples. IEEE Transactions on Pattern Analysis and Machine Intelligence, 45, 12304-12320(2023).

    [58] Jiebin YAN, Yuming FANG, Xuelin LIU. The review of distortion related image quality assessment. Journal of Image and Graphics, 27, 1430-1466(2022).

    [59] J ANTKOWIAK, T J BAINA, F V BARONCINI et al. Final report from the video quality experts group on the validation of objective models of video quality assessment march 2000.

    [60] A MITTAL, A K MOORTHY, A C BOVIK. No-reference image quality assessment in the spatial domain. IEEE Transactions on Image Processing, 21, 4695-4708(2012).

    [61] A MITTAL, R SOUNDARARAJAN, A C BOVIK. Making a “completely blind” image quality analyzer. IEEE Signal Processing Letters, 20, 209-212(2012).

    [62] N ZHENG, M ZHOU, Y DONG et al. Empowering low-light image enhancer through customized learnable priors, 12559-12569(2023).

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    Ziwei LI, Jinlong LIU, Huizhen YANG, Zhiguang ZHANG. Review of low-illuminance image enhancement algorithm based on deep learning[J]. Journal of Applied Optics, 2024, 45(6): 1095

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    Paper Information

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    Received: Oct. 17, 2023

    Accepted: --

    Published Online: Jan. 14, 2025

    The Author Email: Jinlong LIU (刘金龙)

    DOI:10.5768/JAO202445.0609001

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