Let You See in Sand Dust Weather: A Method Based on Halo-Reduced Dark Channel Prior Dehazing for Sand-Dust Image Enhancement

Abstract

The images that are captured in sand storms often suffer from low contrast and serious color cast that are caused by sand dust, and these issues will have significant negative effects on the performance of an outdoor computer vision system. To address these problems, a method based on halo-reduced dark channel prior (DCP) dehazing for sand dust image enhancement is proposed in this paper. It includes three components in sequence: color correction in the LAB color space based on gray world theory, dust removal using a halo-reduced DCP dehazing method, and contrast stretching in the LAB color space using a Gamma function improved contrast limited adaptive histogram equalization (CLAHE), in which a guided filter is used to improve the artifacts of the histogram equalization. Experiments on a large number of real sand dust images demonstrate that the proposed method can well remove the overall yellowing tone and dust haze effect and obtain normal visual colors and a detailed clear image. INDEX TERMSNormalized Gamma correction (NGC), DCP, CLAHE, color correction, LAB color space, illumination enhancement. ZHENGHAO SHI received the B.S. degree in material science and engineering from Dalian Jiaotong University, Dalian, China, in 1995, the M.S. degree in computer application technology from the

On Box-Cox Transformation for Image Normality and Pattern Classification

“…Liu et al [9] proposed a dehazing method where λ is set to 0.5 in the Gamma correction based preprocessing step. Shi et al [10] realised, as we did, that the traditional power-law transformation has the following disadvantage, increasing λ would overcompensate the image's Gamma and thereby darken the processed image while enhancing its contrast. As they did not work out a remedy for such disadvantage, they eventually resorted to using intensity range normalization.…”

“…One of the aims of this paper is to rekindle interest in the Box-Cox algorithm in conjunction with image enhancement. In a wider context, this optimisation algorithm might even help leverage the results of other enhancement algorithms that depend on the parameter λ, such as [10], and/or those setting it arbitrarily for gamma correction [5][6][7][8][9], and in other areas which we did not cover here such as image retrieval where informative features are sought [49]. There are some attempts to devise new methodologies to estimate λ for 1dimensional data transformation, like the work of [50], however, this proposal comes to create an accrual of evidence regarding the utility of the renowned Box-Cox transformation in the imaging field.…”

A Fast Sand-Dust Image Enhancement Algorithm by Blue Channel Compensation and Guided Image Filtering

“…In this section, we conducted a qualitative evaluation and a quantitative evaluation on the processed sand-dust-degraded images to verify the effectiveness of the proposed algorithms. We compared the most representative methods described in Alameen [12], Li et al [4], Fu et al [10], Yang et al [13], Shi et al [22] and Cheng et al [24]. First, we performed a qualitative visual comparison.…”

“…Content may change prior to final publication. [10], (e) Yang et al [13], (f) Shi et al [22], (g) Cheng et al [24] and (h) the proposed method. [10], (e) Yang et al [13], (f) Shi et al [22], (g) Cheng et al [24] and (h) the proposed method.…”

“…The algorithms in [20], [21] estimated the transmission by calculating the difference between the observed intensity and ambient light and were combined with color correction to enhance the sand-dust-degraded images. Shi et al [22] presented a halo-reduced DCP algorithm, which mainly includes three modules: color correction, defogging and contrast enhancement. Very recently, [23] introduced a sand-dust image restoration method based on reversing the blue channel prior.…”