Low-light Color Image Enhancement based on Dark Channel Prior with Retinex Model

Abstract

Low light image enhancement plays the crucial role in night vision applications, and road monitoring systems of artificial intelligence assisted vehicles. But the conventional methods are unable to remove the darkness from source images and resulted in poor visibility performance. Thus, this article proposed an advanced low light image enhancement approach using dark channel prior (DCP). Initially, light reflection (retinex) angles are identified and red channel estimation was used to restore light direction attention. Further, DCP is used to identify the background darkness region with light illumination properties. Then, new anthropic light properties were generated by using transmission map estimation and refinement. Further, image light radiance is recovered by using this updated transmission map values, which generates darkness removed image. Finally, denoising operation is performed to get the best visual quality output image. The simulations conducted on ExDark dataset shows that the proposed method resulted in superior subjective and objective performance as compared to state of art approaches.