Low-level-light image enhancement method based on Retinex theory by improving dual components

In recent years, low-level-light image enhancement technology has attracted much attention, but there are still some problems. For example, sometimes dark areas are not completely improved, and sometimes bright areas near the light source are overexposed. In response to the above issues, an image enhancement method based on Retinex model with dual enhancement of illumination and reflection components was proposed. Firstly, the original image was transformed from RGB space to HSV space, and the V-component was extracted for subsequent processing. Then, the V-component was filtered to obtain the illumination component of the image, and the reflection component of the image was obtained by decomposition according to Retinex theory. Next, the global adaptive brightness enhancement was applied to the illumination component, and the multi-scale detail enhancement was applied to the reflection component. Then, the enhanced illumination component and reflection component were reconstructed according to Retinex model to obtain the V-component reconstruction image, which was processed by nonlinear transformation and local contrast enhancement. Finally, it was converted back to RGB space to obtain the final enhanced image. The experimental results show that the evaluation values of peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM) of this method are 17.741 and 0.765, respectively, which have better image quality and better enhancement effect than other methods.