# Multi-Scale Interactive Network with Color Attention for Low-Light Image Enhancement

## TL;DR

This paper introduces a new network for improving low-light images by combining local and global features with color attention.

## Contribution

The novel MSINet uses a fusion module and color correction branch to enhance low-light images more effectively than existing methods.

## Key findings

- MSINet outperforms state-of-the-art methods in low-light image enhancement.
- The fusion module effectively combines local and global features across different scales.
- Color correction branch ensures accurate color fidelity in enhanced images.

## Abstract

Enhancing low-light images is crucial in computer vision applications. Most existing learning-based models often struggle to balance light enhancement and color correction, while images typically contain different types of information at different levels. Hence, we proposed a multi-scale interactive network with color attention named MSINet to effectively explore these different types of information for lowlight image enhancement (LLIE) tasks. Specifically, the MSINet first employs the CNN-based branch built upon stacked residual channel attention blocks (RCABs) to fully explore the image local features. Meanwhile, the Transformer-based branch constructed by Transformer blocks contains cross-scale attention (CSA) and multi-head self-attention (MHSA) to mine the global features. Notably, the local and global features extracted by each RCAB and Transformer block are interacted with by the fusion module. Additionally, the color correction branch (CCB) based upon self-attention (SA) can learn the color distribution information from the lowlight input for further guaranteeing the color fidelity of the final output. Extensive experiments have demonstrated that our proposed MSINet outperforms state-of-the-art LLIE methods in light enhancement and color correction.