Research On Low-Illumination Image Enhancement Algorithm

Low illumination image enhancement is an important technology to improve image quality by enhancing the overall or partial area contrast of the whole or local area of the image,and it effectively improves the visual effect of the image.However,current low-illuminance images still have three main shortcomings that need to be solved:First,the complex lighting conditions make the image illuminance uneven,and the enhancement results will have over-enhancement or under-enhancement problems.Second,the interference information in the image will increase with the contrast.The increase of is magnified,and thirdly,the noise with larger amplitude will obscure the details of the image.In response to the above three problems,three low-light image enhancement algorithms are proposed.The main research contents and results are as follows:(1)In order to solve the problem of complex lighting conditions in the image,an adaptive gamma correction algorithm based on information entropy was proposed.The algorithm calculates the optimal parameters of gamma correction by maximizing the information entropy to realize the adaptive adjustment of image contrast,effectively avoiding the problems of over-enhancement and under-enhancement.In addition,the algorithm has higher operating efficiency and better real-time performance.According to experimental comparison and analysis,result show that compared with other six classic image enhancement algorithms,the proposed algorithm has better results in both subjective vision and objective indicators.(2)Aiming at the problem that the interference information in the image is amplified,an adaptive enhancement algorithm based on robust principal component analysis(RPCA)was proposed.The algorithm uses RPCA decomposition to separate the illuminance information and noise to obtain low-rank components and sparse components,and uses the above-mentioned adaptive gamma correction algorithm for low-rank components to improve image contrast.The proposed algorithm adequately considers the spatial relationship between pixels,and has strong robustness to noise with larger amplitude.According to experimental comparison and analysis,results show that,compared with the other four classic image enhancement algorithms,the proposed algorithm has better results in both subjective vision and objective indicators.(3)Aiming at the problem of a large amount of noise in low signal-to-noise ratio images,an adaptive enhancement algorithm based on denoising self-encoding network was proposed.The algorithm uses the denoising autoencoder as the network model,and selects the sparse components in the RPCA decomposition model as the training set to separates the detailed information and noise which belong to high-frequency components to realizes noise suppression,and further trains them through step-by-step training.Improve the noise suppression effect of the network.According to experimental comparison and analysis,results show that the proposed algorithm has better results in both subjective vision and objective indicators compared with the other two classic noise reduction algorithms.