Visual Attention and ODE-inspired Fusion Network for image dehazing

Abstract

Image dehazing is an improtant image pre-processing step for many computer vision tasks with many proposed methods using convolutional neural networks. Ordinary Differential Equations (ODE) are a powerful mathematical tool in social and natural science, leading to better understanding, prediction, and use of information systems. Because prior knowledge has demonstrated its effectiveness in many practical domains, we design a Visual Attention Network (VAN) and an ODE-inspired Network (ODEN) based on prior knowledge. Then, we develop a multi-model fuzzy fusion strategy, which integrates results predicted by the prior-based Visual Attention Network (VAN) and the ODE-inspired Network (ODEN) into a single network to leverage their respective strengths in improving the dehazing performance. First, to utilize the haze-related prior for dehazing, the VAN removes haze with the help of the haze attention map. Then, to improve performance of stacked residual blocks inspired by the first-order Euler method in ODE-based methods, the ODEN is built by only 3 Runge–Kutta Modules (RKM), each of which not only is related to the stable fourth-order Runge–Kutta method implemented in the Runge–Kutta Block (RKB), but also combines the RKB with an attention mechanism. Finally, an attention based fusion mechanism is used to fuse results estimated by the ODEN and the VAN based on the multi-level features extracted by a pretrained ResNeXt. The experimental results demonstrate that the proposed method outperforms existing state-of-the-art methods in terms of visual effects and accuracy. The average PSNR and SSIM on three public datasets are 31.36 and 0.9766, respectively, which are better than the compared state-of-the-art methods.