Iterative Retinex-Based Decomposition Framework for Low Light Visibility Restoration
Iterative Retinex-Based Decomposition Framework for Low Light Visibility Restoration
Blog Article
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 Cosmetic Bag 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 High Chairs potential to substantially impact human perception and enhance the performance of numerous computer vision applications.