Two-Layer Gaussian Process Regression With Example Selection for Image Dehazing

Researchers have devoted great efforts to image dehazing with prior assumptions in the past decade. Recently developed example-based approaches typically lack elegant models for the hazy process and meanwhile demand synthetic hazy images by manual selection. The priors from observations, and those trained from synthetic images cannot always reflect true structural information of natural images in practice. In this paper, we present a learning model for haze removal by using two-layer Gaussian process regression (GPR). By using training examples, the two-layer GPR establishes a direct relationship from the input image to the depth-dependent transmission, and learns local image priors to further improve the estimation. We also provide a systematic scheme to automatically collect suitable training pairs, which works for both simulated examples and images of natural scenes. Both qualitative and quantitative comparisons on real-world and synthetic hazy images demonstrate the effectiveness of the proposed approach, especially for white or bright objects and heavy haze regions in which traditional methods may fail.