Joint Haze relevant Features Selection and Transmission Estimation via Deep Belief Network for Efficient Single Image Dehazing
Haze relevant image features are widely used in haze density perception and transmission estimation for single image dehazing, since hazy images usually have distinct features or characters from clean images. However, little attention has been paid to identify haze relevant features, and further select some compact but informative image features for image dehazing. In this paper, we propose a novel joint feature selection and transmission estimation model named JFSTE via Deep Belief Network (DBN) for image dehazing. First, we develop a one to one linear feature selection layer in the DBN, in which each input feature only connects to one node in next layer with a binary weight. Meanwhile, a simple feature selection strategy is proposed to determine these binary weights by considering the importance of features and the expected selected feature numbers. Second, in order to identify haze relevant features of hazy image and narrow down their relevance, the minimum redundancy maximum relevancy is introduced into the joint optimization of our network. A comparative study with state of the art approaches manifests a competitive performance of our proposed method.
2018 24th International Conference on Pattern Recognition (ICPR)