SurroundNet: Towards effective low-light image enhancement

Although Convolution Neural Networks (CNNs) have made substantial progress in the low-light image enhancement task, one critical problem of CNNs is the paradox of model complexity and performance. This paper presents a novel SurroundNet that only involves less than 150K parameters (about 80–98 percent size reduction compared to SOTAs) and achieves very competitive performance. The proposed network comprises several Adaptive Retinex Blocks (ARBlock), which can be viewed as a novel extension of Single Scale Retinex in feature space. The core of our ARBlock is an efficient illumination estimation function called Adaptive Surround Function (ASF). It can be regarded as a general form of surround functions and be implemented by convolution layers. In addition, we also introduce a Low-Exposure Denoiser (LED) to smooth the low-light image before the enhancement. We evaluate the proposed method on two real-world low-light datasets. Experimental results demonstrate the superiority of our submitted SurroundNet in both performance and network parameters against State-of-the-Art low-light image enhancement methods. The code is available at https://github.com/ouc-ocean-group/SurroundNet.