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Geometric-Pixel Guided Single-Pass Convolution Neural Network With Graph Cut for Image Dehazing

One of the major shortcomings of existing image dehazing algorithms is in estimating scene transmittance, which has assumed many items in the existing algorithms. One key assumption has been pixel uniformity and smoothness. In this paper, we propose to solve the dehazing problem using a combination of single-pass CNN with graph cut algorithms. It considers the transmittance based on differential pixel-based variance, local and global patches and energy functions to improve the transmission map. The proposed algorithm was tested on different images and evaluated based on various evaluation metrics. Our results show more details when compared to four existing benchmark enhancement methods. The proposed method has one major drawback: the over-bright areas tend to lose some features in the final image.