MDIE-Net: Multi-Scale Detailed Information Exploration Network for Low-Light Image Enhancement
TL;DR
A multi-scale detailed information exploration network is proposed that outperforms other state-of-the-art image enhancement approaches in both visual effects and evaluation metrics.
Abstract
Low-light image enhancement technology has significant application value in various fields. To improve the image quality under challenging conditions, the recently proposed approaches effectively extract deep feature representations from spatial or frequency domain via well-designed neural networks. Although the overall brightness and the critical structural information of images have been carefully restored, the detailed information is still easy to lose. To address this issue, a multi-scale detailed information exploration network is proposed. First, a Haar wavelet encoding (HWE) block is designed, which consists of the proposed multi-scale feature enhancement (MFE) module and the Haar wavelet transform. In this way, more intrinsic structural information of input low-light images is extracted. Furthermore, a cross-scale valuable information exploration (CVIE) module is constructed, establishing the information interactions between the frequency and spatial domains across different scales. Thus, more illumination and texture details of images are progressively restored. Finally, an edge enhancement decoding (EED) block is devised to further preserve the boundary information of low-light images during the reconstruction process. Qualitative and quantitative experiments are conducted on three widely used public datasets, including both real-world indoor and outdoor scenarios. The experimental results demonstrate that the proposed network outperforms other state-of-the-art image enhancement approaches in both visual effects and evaluation metrics.