Abstract
Dark channel prior (DCP) has been widely used in single image defogging because of its simple implementation and satisfactory performance. This paper addresses the shortcomings of the DCP-based defogging algorithm and proposes an optimized method by using an adaptive fusion mechanism. This proposed method makes full use of the smoothing and “squeezing” characteristics of the Logistic Function to obtain more reasonable dark channels avoiding further refining the transmission map. In addition, a maximum filtering on dark channels is taken to improve the accuracy of dark channels around the object boundaries and the overall brightness of the defogged clear images. Meanwhile, the location information and brightness information of fog image are weighed to obtain more accurate atmosphere light. Quantitative and qualitative comparisons show that the proposed method outperforms state-of-the-art image defogging algorithms.
Highlights
(1) Maximum operation is performed on the dark channels to increase the brightness of the defogged clear images and improve the accuracy of the dark channels around the object edges (2) A nonlinear weighted average is executed using the Logistic Function on two typical dark channels that are calculated by window regions with different radii, respectively (3) Both location information and brightness information of foggy images are weighed to get a more accurate atmosphere light e rest of this paper is organized as follows
To overcome the above shortcomings, this paper proposes a novel method based on Dark channel prior (DCP) for achieving the excellent defogging effect. e major contributions of this paper are as follows: (1) Maximum operation is performed on the dark channels to increase the brightness of the defogged clear images and improve the accuracy of the dark channels around the object edges
Deep convolution neural network (CNN) has made great progress in many machine vision tasks, which drives researchers to apply deep learning network to image defogging. e deep learning-based defogging method has two mainstream designs: one is estimating the transmission map t (x) and the atmospheric light A by neural network and removing fog from images combining with the atmospheric degradation model; the other is designing an end-to-end training model, which takes the foggy image as the input and directly outputs a defogged clear image
Summary
Deep convolution neural network (CNN) has made great progress in many machine vision tasks, which drives researchers to apply deep learning network to image defogging. e deep learning-based defogging method has two mainstream designs: one is estimating the transmission map t (x) and the atmospheric light A by neural network and removing fog from images combining with the atmospheric degradation model; the other is designing an end-to-end training model, which takes the foggy image as the input and directly outputs a defogged clear image. Xiao et al [16] proposed a new haze layer-based single image defogging algorithm; firstly, it obtains a residual image by hazy layers through an end-to-end mapping from the original foggy images and designs the CNN-based model to remove the residual image from the given hazy image to obtain a recovered defogged clear image. Different from the former design, the latter one attempts to avoid the time-consuming and well-known challenging estimation of t (x) and A. There is still room for optimization in current defogging methods
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