Iterative Retinex-Based Decomposition Framework for Low Light Visibility Restoration

Images captured in low-light conditions often suffer from low brightness, low signal-to-noise ratio, low contrast, a narrow gray range, and color distortion, which can significantly impact human perception and limit the performance of various computer vision applications. Most existing low-light image restoration methods require assistance with a color cast, local over-exposure, glow, and artificial light sources. This paper proposes a new framework called RSD-Net, incorporating several innovative blocks, including a novel iterative Retinex network decomposition and enhancement algorithms, to improve the visibility and quality of images captured in low-light or nighttime conditions. We have extensively evaluated our proposed method on various benchmarking datasets and under different real-world scenarios, including challenging conditions such as glow, artificial light sources, low illumination, and noise. Moreover, we have evaluated our method on a face detection algorithm using extremely dark images and compared its performance with other state-of-the-art methods. The simulation results show that our proposed framework achieves a noticeable improvement compared to other low-quality image restoration techniques and enhances face detection accuracy in low-quality environments. The proposed framework has the potential to substantially impact human perception and enhance the performance of numerous computer vision applications.

Automated summary

Images captured in low-light conditions often suffer from issues such as low brightness, low signal-to-noise ratio, and color distortion. This paper proposes a new framework called RSD-Net, which includes innovative algorithms to enhance the visibility and quality of low-light images. The proposed framework achieves significant improvement compared to other methods and enhances face detection accuracy in low-quality environments.