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A novel strategy is proposed to address block artifacts in a conventional dark channel prior (DCP). The DCP was used to estimate the transmission map based on patch-based processing, which also results in image blurring. To enhance a degraded image, the proposed single-image dehazing technique restores a blurred image with a refined DCP based on a hidden Markov random field. Therefore, the proposed algorithm estimates a refined transmission map that can reduce the block artifacts and improve the image clarity without explicit guided filters. Experiments were performed on the remote-sensing images. The results confirm that the proposed algorithm is superior to the conventional approaches to image haze removal. Moreover, the proposed algorithm is suitable for image matching based on local feature extraction.

Haze Removal Based on Refined Transmission Map for Aerial Image Matching