SCRNet: a Retinex Structure-based Low-light Enhancement Model Guided by
  Spatial Consistency
Miao Zhang Yiqing Shen Shenghui Zhong
Images captured under low-light conditions are often plagued by several
challenges, including diminished contrast, increased noise, loss of fine
details, and unnatural color reproduction. These factors can significantly
hinder the performance of computer vision tasks such as object detection and
image segmentation. As a result, improving the quality of low-light images is
of paramount importance for practical applications in the computer vision
domain.To effectively address these challenges, we present a novel low-light
image enhancement model, termed Spatial Consistency Retinex Network (SCRNet),
which leverages the Retinex-based structure and is guided by the principle of
spatial consistency.Specifically, our proposed model incorporates three levels
of consistency: channel level, semantic level, and texture level, inspired by
the principle of spatial consistency.These levels of consistency enable our
model to adaptively enhance image features, ensuring more accurate and visually
pleasing results.Extensive experimental evaluations on various low-light image
datasets demonstrate that our proposed SCRNet outshines existing
state-of-the-art methods, highlighting the potential of SCRNet as an effective
solution for enhancing low-light images.