MSNet: A novel end‐to‐end single image dehazing network with multiple inter‐scale dense skip‐connections
Qiaosi Yi, Aiwen Jiang, Xiaolin Deng, Changhong Liu · IET Image Processing · 2020
Abstract Dehazing is a challenging ill‐posed image restoration task. Various prior‐based and learning‐based methods have been proposed. Among them, end‐to‐end deep models achieve great success on performance improvement. However, most of them are concentrated on feature learning within the same block scale in isolation, and cannot perform associated analysis well on feature characteristics of different scales. Inter‐scale information reuse which is especially beneficial to image restoration is often neglected. Therefore, in this paper, a novel end‐to‐end network with multiple inter‐scale dense skip‐connections for image dehazing is proposed. Sufficient complementary information combination is considered through dense inter‐scale skip‐connections among encoder and decoder block layers. Besides avoiding gradient vanishing, a kind of bottleneck residual block is proposed to control the importance of local gradients at different scales over global learning process. Extensive comparisons and ablation studies on public dehazing datasets and real‐world images have been conducted. The experiment results demonstrate that the proposed novel elements can ensure more stable training process and superior testing performance with great improvements on PSNR and SSIM. Authors' haze‐removal results consistently comply satisfactorily with real situations, having much higher definition and contrast without colour distortion than those from the state‐of‐the‐art methods compared in this paper.