DEEP LOW LIGHT IMAGE ENHANCEMENT VIA MULTI-SCALE RECURSIVE FEATURE ENHANCEMENT AND CURVE ADJUSTMENT

Photographs taken in low-illumination environment have a low signal-to-noise ratio and impaired visual quality. Enhancing lowlight images tends to amplify noise. To address this problem, we propose a Multi-Scale Recursive Feature Enhancement (MSRFE) network for low light image enhancement. The MSRFE network consists of several Feature Enhancement (FE) block which are applied to enhance the multi-scale image feature and remove the noise recursively in each scale residual map between adjacent scale feature. Then, a deep recursive curve adjustment (CA) block is proposed further fine-tunes the output of MSRFE netowrk by learning a non-linear curve which recovers the image details. We evaluate the proposed method on both real and synthetic datasets. The results show that our proposed method outperforms other state-of-the-art methods on both visual and objective evaluation indicators.