Haze removal using a hybrid convolutional sparse representation model

Abstract

Haze removal is a challenging task in image recovery, because hazy images are always degraded by turbid media in atmosphere, showing limited visibility and low contrast. Analysis Sparse Representation (ASR) and Synthesis Sparse Representation (SSR) has been widely used to recover degraded images. But there are always unexpected noise and details loss in the recovered images, as they take relatively less account of the images’ inherent coherence between image patches. Thus, in this paper, we propose a new haze removal method based on hybrid convolutional sparse representation, with consideration of the adjacent relationship by convolution and superposition. To integrate optical model into a convolutional sparse framework, we separate transmission map by transforming it into logarithm domain. And then a structure-based constraint on transmission map is proposed to maintain piece-wise smoothness and reduce the influence brought by pseudo depth abrupt edges. Experiment results demonstrate that the proposed method can restore fine structure of hazy images and suppress boosted noise.