AMBCR: Low‐light image enhancement via attention guided multi‐branch construction and Retinex theory

Abstract

Abstract Due to different lighting environments and equipment limitations, low‐light images have high noise, low contrast and unobvious colours. The main purpose of low‐light image enhancement is to preserve the details and suppress noise as much as possible while improving the contrast of the image. Here, different networks are first combined to construct a multi‐branch module for features extraction, and use the module and Retinex theory to extract the reflection map of the image. Then an attention mechanism is introduced into the multi‐branch construction to balance the feature weight of each branch, and get the final result by the reconstruction module. The Retinex theory is used to calculate the L1 loss and the gradient loss for the intermediate feature map of the entire model to train our framework. The entire process is completed in an end‐to‐end‐way, which avoids the hand‐crafted reconstruction rules and reduces the workload. What's more, a large number of experiments demonstrate that the proposed framework performs better results than state‐of‐the‐art algorithms in both quantitative and qualitative evaluations of image enhancement.

Keywords

• attention networks
• low‐light image enhancement
• multi‐branch construction
• Retinex theory