Inverse Atmoshperic Scattering Modeling with Convolutional Neural Networks for Single Image Dehazing

Abstract

Single image dehazing is an ill-posed problem. Most existing works use the atmospheric scattering model (ASM) [1] and some natural priors to dehazing. Recently, DehazeNet [2] was developed using deep learning approach achieves the state-of-the-art results on many test hazy images, which motivates us to adopt the deep learning approach in this study. After carefully research on the essential principle of dehazing using ASM, we develop a novel end-to-end convolutional neural network to efficiently implement the inverse ASM for single image dehazing, which is termed as IASM -Net. Specifically, the ASM is equivalently decomposed into three sub-model and the IASM-Net is designed to model these sub-model efficiently. Our study shows that IASM-Net is jointly optimized by minimizing the model error between the output and the clear ground truth, which gives better performance compared existing CNN-based transmission model approach [1], [2]. To facilitate this study, a sufficient aerial image set containing more than 6K aerial photos is built to train and test our IASM-Net. Experimental results validate the effectiveness and efficiency of our well designed IASM -Net.