An improved algorithm using weighted guided coefficient and union self‐adaptive image enhancement for single image haze removal

Abstract

The visibility of outdoor images is usually significantly degraded by haze. Existing dehazing algorithms, such as dark channel prior (DCP) and colour attenuation prior (CAP), have made great progress and are highly effective. However, they all suffer from the problems of dark distortion and detailed information loss. This paper proposes an improved algorithm for single-image haze removal based on dark channel prior with weighted guided coefficient and union self-adaptive image enhancement. First, a weighted guided coefficient method with sampling based on guided image filtering is proposed to refine the transmission map efficiently. Second, the k-means clustering method is adopted to calibrate the original image into bright and non-bright colour areas and form a transmission constraint matrix. The constraint matrix is then marked by connected-component labelling, and small bright regions are eliminated to form an atmospheric light constraint matrix, which can suppress the halo effect and optimize the atmospheric light. Finally, an adaptive linear contrast enhancement algorithm with a union score is proposed to optimize restored images. Experimental results demonstrate that the proposed algorithm can overcome the problems of image distortion and detailed information loss and is more efficient than conventional dehazing algorithms.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

Visual Communication Design Based on Sparsity-Enhanced Image Processing Models

“…Yang et al proposed a nonlinear anisotropic diffusion system combined with time-delay regularization to construct a structure tensor for image enhancement and segmentation, and verified the effectiveness of the method by Galerkin's method [13]. Zhou et al proposed an improved single-image defogging algorithm based on weighted guidance coefficients for the visibility degradation of outdoor images due to haze, and combined it with joint adaptive image enhancement, and the experimental The results show that the algorithm can effectively overcome image distortion and loss of detail information, and the efficiency exceeds that of the traditional dehaze algorithm [14]. Peng et al proposed an attenuated image enhancement method with adaptive color compensation and detail optimization for color compensation and loss of local detail information in underwater image enhancement, and the study proved that the method can effectively enhance the contrast, detail information, and balance the color [15].…”

Image haze removal based on rolling deep learning and Retinex theory

“…This brings a great impact on outdoor camera photography, automatic driving, target tracking, navigation and optical remote sensing, so removing the impact of haze in optical image and video is an important part of image pre-processing. So different scholars have studied the removal of image haze according to their needs, and put forward many filtering methods [1][2][3][4][5][6][7][8][9][10]. These methods can be summarized into three classes.…”