Aerial Image Dehazing Based on Boundary Constraint and Color Correction for Target Detection

Abstract

Abstract Detecting objects in aerial images is very important for surveillance, security and military applications. The quality of hazy aerial images is severely degraded because the image acquisition device is far away from the ground target. Due to the small change in scene depth, the atmospheric light estimation is prone to deviation. Therefore, traditional image dehazing methods cannot achieve satisfactory results. In this paper, we design a dehazing algorithm based on boundary constraint and color correction to enhance image details and improve accuracy of target detection. The boundary constraint is used to obtain the medium transmission of the structure layer after image decomposition. The transmission is optimized by the context regularization based on the weighted L1 norm to obtain a dehazed structure layer with clear edges. Then the dehazed structure layer and the enhanced texture layer are combined, and the image brightness is adjusted through blind inverse gamma correction to improve the visual effect. Experiments show that our algorithm can enhance the contrast of aerial images and is better than other methods in improving the accuracy of target detection in hazy aerial images.