Retinex-based　methods　have　become　a　general　approach　for　solving　low-light　image　enhancement　(LLIE).　However,　traditional　methods　require　post-processing　of　illumination　(e.g.,　gamma　correction),　which　lacks　adaptability　and　disrupts　the　illumination　structure.　Retinex-based　deep　networks　typically　follow　a　‘decomposition-adjustment-exposure　control’　process,　which　is　redundant　and　lacks　robustness.　One　major　issue　is　the　inaccuracy　in　estimating　and　decomposing　the　initial　illumination.　Accurate　initial　illumination　can　prevent　further　post-processing　instability.　We　propose　IniRetinex,　rethinking　the　Retinex-based　LLIE　method　from　the　perspective　of　initialization.　By　using　neural　networks　to　provide　reasonable　initial　illumination　and　solving　for　smooth　illumination　through　optimization,　higher　performance　LLIE　is　achieved.　We　construct　a　two-layer　convolutional　neural　network　to　capture　the　low-frequency　structure　of　the　image,　adaptively　compensating　for　classical　initial　illumination　and　avoiding　additional　post-processing.　The　network　requires　no　pre-training　and　can　be　implemented　in　an　unsupervised　manner　with　just　a　few　iterations,　making　it　highly　efficient.　Additionally,　we　propose　a　new　illumination　optimization　strategy　by　introducing　an　additional　proximal　penalty　term,　improving　illumination　in　areas　with　varying　levels　and　enhancing　image　details.　Extensive　experiments　on　various　low-light　image　datasets　demonstrate　that　our　method　achieves　state-of-the-art　(SOTA)　results　on　multiple　benchmarks,　offering　higher　stability　and　inference　efficiency　compared　to　current　advanced　methods.　©　2025,　Association　for　the　Advancement　of　Artificial　Intelligence　(www.aaai.org).　All　rights　reserved.