FFA-Net: Feature Fusion Attention Network for Single Image Dehazing

TL;DR

FFA-Net introduces a novel feature fusion attention mechanism with channel and pixel attention to effectively restore haze-free images, significantly outperforming previous methods in image dehazing tasks.

Contribution

The paper presents a new end-to-end network with feature attention modules and a multi-level feature fusion structure for improved single image dehazing.

Findings

Achieved a PSNR of 36.39dB on SOTS indoor dataset, surpassing previous state-of-the-art.

Demonstrated superior qualitative and quantitative dehazing performance.

Introduced a flexible feature attention mechanism combining channel and pixel attention.

Abstract

In this paper, we propose an end-to-end feature fusion at-tention network (FFA-Net) to directly restore the haze-free image. The FFA-Net architecture consists of three key components: 1) A novel Feature Attention (FA) module combines Channel Attention with Pixel Attention mechanism, considering that different channel-wise features contain totally different weighted information and haze distribution is uneven on the different image pixels. FA treats different features and pixels unequally, which provides additional flexibility in dealing with different types of information, expanding the representational ability of CNNs. 2) A basic block structure consists of Local Residual Learning and Feature Attention, Local Residual Learning allowing the less important information such as thin haze region or low-frequency to be bypassed through multiple local residual connections, let main network architecture focus on more effective information. 3) An Attention-based different levels Feature Fusion (FFA) structure, the feature weights are adaptively learned from the Feature Attention (FA) module, giving more weight to important features. This structure can also retain the information of shallow layers and pass it into deep layers. The experimental results demonstrate that our proposed FFA-Net surpasses previous state-of-the-art single image dehazing methods by a very large margin both quantitatively and qualitatively, boosting the best published PSNR metric from 30.23db to 36.39db on the SOTS indoor test dataset. Code has been made available at GitHub.