| Citation: | WU Qingbo, WANG Rui, REN Wenqiet al. Learning shrinkage fields for low-light image enhancement via Retinex[J]. Journal of Beijing University of Aeronautics and Astronautics, 2020, 46(9): 1711-1720. doi: 10.13700/j.bh.1001-5965.2020.0064(in Chinese)
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To enhance a low-light image and mitigate noises simultaneously, we propose an image enhancement algorithm by learning Shrinkage Field (SF) to improve the reflectance image and the illumination map in Retinex model. To this end, first, we design a novel objective function by constraining the latent reflectance image and the illumination map with two different groups of high-order filters in regularization terms. These filters can be learnt to possess various activation patterns and thus facilitate recovering the reflectance image and suppressing noises at the same time. Then, we update the latent variables in the objective function optimization by calculating SFs, where the parameterized shrinkage functions are capable to scale the responses of the corresponding high-order filters convolving the reflectance image and the illumination map. Finally, we embed an auxiliary SF model before the update of the illumination map in each cascade to suppress the propagation of noises and undesirable artifacts and further to refine the estimation of illumination map. Experimental results demonstrate that the proposed algorithm outperforms the state-of-the-art low-light image enhancement methods in terms of Peak Signal to Noise Ratio(PSNR) and Structural Similarity (SSIM).
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