基于深度學習和Retinex理論的圖像增強方法
打開文本圖片集
引用格式：.基于深度學習和Retinex理論的圖像增強方法[J].現(xiàn)代電子技術，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
當光線不足時，照片質量往往會降低，表現(xiàn)為可見度減弱、色彩失真，以及細節(jié)模糊和不自然的色彩再現(xiàn)[1。（剩余12061字）