Efficient Re-Parameterization Residual Attention Network for Nonhomogeneous Image Dehazing

Abstract

Real-world nonhomogeneous haze brings challenges to image restoration. More efforts are needed to remove dense haze and thin haze simultaneously and efficiently. However, most existing dehazing methods do not pay attention to the complex distributions of haze and usually suffer from a low runtime speed. To tackle such problems, we present an efficient re-parameterization residual attention network (RRA-Net), whose design has three key aspects. Firstly, we propose a training-time multi-branch residual attention block (MRAB), where multi-scale convolutions in different branches cope with the nonuniformity of haze and are converted into a single-path convolution during inference. It also features local residual learning with improved spatial attention and channel attention, allowing dense and thin haze to be attended to differently. Secondly, our lightweight network structure cascades six MRABs followed by a long skip connection with attention and a fusion tail. Overall, our RRA-Net only has about 0.3M parameters. Thirdly, two new loss functions, namely the Laplace pyramid loss and the color attenuation loss, help train the network to recover details and colors. The experimental results show that the proposed RRA-Net performs favorably against state-of-the-art dehazing methods on real-world image datasets, including both nonhomogeneous haze and dense homogeneous haze. A runtime comparison under the same hardware setup also demonstrates the superior efficiency of the proposed network.

Keywords

• image dehazing
• nonhomogeneous haze
• re-parameterization network
• residual attention block