基于深度学习和Retinex理论的图像增强方法

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引用格式：.基于深度学习和Retinex理论的图像增强方法[J].现代电子技术，2025，48（（13）：36-42.

Image enhancement method based on deep learning and Retinex theory

WANG Dexu，WANG Zhifeng，YANWenqiang （Schoolof IntelligentManufacturingandControlEnginering，ShanghaiPolytechnicUniversity，ShanghaiO2oo，Chna）

Abstract：Thisstudyaimstoimproveimagequalityunderlow-lightconditions，primarilyaddressingissuesofreduced visibilityandcolordistortion.BycombiningimageprocesingtechniquesbasedonRetinextheoryandadvancedneuralnetwork algorithms，aninnovativeimageenhancementframeworkisproposed.Thisframeworkconsistsoftwoparts，namedanimage decompositionnetworkandanimageenhancementnetwork.The former isresponsiblefordecomposing theoriginal image into illuminationcomponentandreflectancecomponent，whilethelaterisresponsibleforoptimizing parametersandperformingY correctinbythenaturalimagequalityevaluator（NIQE），adjustingthebrightnessandcontrastoftheilluminationcomponent， andthenre-fusing theilluminationcomponent with thereflectancecomponent，soastoenhanceoverallimagequality.Testson standard datasetsLOLandLOL-V2showthatthe proposed methodoutperformsmost existing image enhancement algorithms in termsof thepeak signal-to-noiseratio （PSNR）andthestructural similarityindex measure （SIM），whichdemonstratesits effectiveness and practicalityin the field of low-light image enhancement.

Keywords：neural network;Retinex theory; NIQE; γ correction; PSNR; SSIM; image enhancement; image decomposition

0 引言

当光线不足时，照片质量往往会降低，表现为可见度减弱、色彩失真，以及细节模糊和不自然的色彩再现[1。（剩余12061字）