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) blocks 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 can adjust the image luminance and 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.