The paper "Kindling the Darkness: A Practical Low-light Image Enhancer" by Yonghua Zhang, Jiawan Zhang, and Xiaojie Guo addresses the challenge of enhancing low-light images. Traditional methods often suffer from noise amplification, color distortion, and limited flexibility in adjusting light levels. The authors propose a deep neural network called KinD, which decomposes images into reflectance and illumination components, inspired by Retinex theory. This decomposition allows for better regularization and learning of the image components. KinD is trained using paired images under different exposure conditions, avoiding the need for ground-truth reflectance and illumination information. The network includes a reflectance restoration module to remove degradations and an illumination adjustment module to flexibly adjust light levels. Extensive experiments on various datasets demonstrate the effectiveness and superiority of KinD over state-of-the-art methods, showing improved visual quality and practical efficiency.The paper "Kindling the Darkness: A Practical Low-light Image Enhancer" by Yonghua Zhang, Jiawan Zhang, and Xiaojie Guo addresses the challenge of enhancing low-light images. Traditional methods often suffer from noise amplification, color distortion, and limited flexibility in adjusting light levels. The authors propose a deep neural network called KinD, which decomposes images into reflectance and illumination components, inspired by Retinex theory. This decomposition allows for better regularization and learning of the image components. KinD is trained using paired images under different exposure conditions, avoiding the need for ground-truth reflectance and illumination information. The network includes a reflectance restoration module to remove degradations and an illumination adjustment module to flexibly adjust light levels. Extensive experiments on various datasets demonstrate the effectiveness and superiority of KinD over state-of-the-art methods, showing improved visual quality and practical efficiency.