Self-guided disentangled representation learning for single image dehazing

Abstract

Image dehazing has received extensive research attention as images collected in hazy weather are limited by low visibility and information dropout. Recently, disentangled representation learning has made excellent progress in various vision tasks. However, existing networks for low-level vision tasks lack efficient feature interaction and delivery mechanisms in the disentanglement process or an evaluation mechanism for the degree of decoupling in the reconstruction process, rendering direct application to image dehazing challenging. We propose a self-guided disentangled representation learning (SGDRL) algorithm with a self-guided disentangled network to realize multi-level progressive feature decoupling through sharing and interaction. The self-guided disentangled (SGD) network extracts image features using the multi-layer backbone network, and attribute features are weighted using the self-guided attention mechanism for the backbone features. In addition, we introduce a disentanglement-guided (DG) module to evaluate the degree of feature decomposition and guide the feature fusion process in the reconstruction stage. Accordingly, we develop SGDRL-based unsupervised and semi-supervised single image dehazing networks. Extensive experiments demonstrate the superiority of the proposed method for real-world image dehazing. The source code is available at https://github.com/dehazing/SGDRL.