High-order Adams Network (HIAN) for image dehazing

Abstract

Convolutional Neural Networks (CNN) are widely used in image dehazing. However, existing network frameworks are built based on manual design from practical experience, lacking interpretable result or theoretical guidelines. Recently, residual networks are regarded as the explicit Euler forward approximation of the ODE (Ordinary Differential Equation), and several ODE-inspired networks are proposed based on the low-order explicit Euler schemes. However, on the issues of system stability and training convergence, high-order Implicit Adams Predictor–Corrector (IAPC) methods have proven to be better than low-order explicit Euler methods. Hence, we extend the IAPC method to the High-order Implicit Adams Network (HIAN). To do so, we design a series of Implicit Adams Predictor–Corrector Blocks (IABs) based on the high-order IAPC methods, all of which give better stability and accuracy than the ones designed using the low-order Euler methods. Given that, we further propose the Implicit Adams Predictor–Corrector Module (IAM) by combining the Non-local Sparse Attention (NSA) and Attention Feature Fusion (AFF) with stacked IABs where the NSA explores the mutual-correlation among intermediate features with low computation cost via a sparse constraint, while the AFF fuses intermediate features by reweighting the features from stacked IABs adaptively. Moreover, because manual network design with IABs limits dehazing performance, the Neural Architecture Search (NAS) is used to find an optimal architecture automatically. This resulting design not only is interpretable for image dehazing but also provides a reliable guideline on future network designs. The experiments demonstrate that the proposed method outperforms most existing methods on both synthetic and real images.