Image dehazing using two-dimensional canonical correlation analysis

Image dehazing is an important issue that interests both image processing and computer vision. In this study, image dehazing is modelled as an example-based learning problem, and a novel dehazing algorithm using two-dimensional (2D) canonical correlation analysis (CCA) is proposed. By assuming that the hazy-free image patches are smooth and the pixel intensities in the same patch are approximate to constant, the authors deduce an underlying linear correlation between the observed hazy image patches and corresponding transmission patches. By maximising the correlation between the patch-pairs of hazy image and corresponding transmission map, 2D CCA is able to learn a subspace to reconstruct the reliable transmission. Thus, given a test hazy image, the transmission map is aggregated by the nearest neighbour patches in the subspace and then globally refined by a local mean adaptive guided filter. The final hazy-free image is obtained by using the dichromatic atmospheric model. Experimental results demonstrate the efficiency of the proposed method in single image dehazing.