Blind Haze Separation

Shwartz, Sarit; Namer, Einav; Schechner, Yoav Y.

doi:10.1109/cvpr.2006.71

Cited by 443 publications

(248 citation statements)

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“…Some methods [8,9] employ multiple images of the same scene, taken under various atmospheric conditions or combined with near-infrared version [10]. Polarization methods [11][12][13] exploit the fact that airlight is partially polarized. By taking the difference of two images of the same scene under different polarization angles, it becomes possible to estimate the magnitude of the polarized haze light component.…”

Section: Related Workmentioning

confidence: 99%

A Fast Semi-inverse Approach to Detect and Remove the Haze from a Single Image

Ancuti

Hermans

Bekaert

2011

Lecture Notes in Computer Science

147

116

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Abstract. In this paper we introduce a novel approach to restore a single image degraded by atmospheric phenomena such as fog or haze. The presented algorithm allows for fast identification of hazy regions of an image, without making use of expensive optimization and refinement procedures. By applying a single per pixel operation on the original image, we produce a 'semi-inverse' of the image. Based on the hue disparity between the original image and its semi-inverse, we are then able to identify hazy regions on a per pixel basis. This enables for a simple estimation of the airlight constant and the transmission map. Our approach is based on an extensive study on a large data set of images, and validated based on a metric that measures the contrast but also the structural changes. The algorithm is straightforward and performs faster than existing strategies while yielding comparative and even better results. We also provide a comparative evaluation against other recent single image dehazing methods, demonstrating the efficiency and utility of our approach.

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Section: Related Workmentioning

confidence: 99%

A Fast Semi-inverse Approach to Detect and Remove the Haze from a Single Image

Ancuti

Hermans

Bekaert

2011

Lecture Notes in Computer Science

147

116

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“…When using an atmospheric scattering model, it is critical to estimate the scene depth accurately. The literature [4][5][6] proposed using multiple images or external information to derive the scene depth map; however, this requirement is difficult to fulfill in many realworld applications.…”

Section: Introductionmentioning

confidence: 99%

Single image dehazing via an improved atmospheric scattering model

Wang

2016

Vis Comput

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Under foggy or hazy weather conditions, the visibility and color fidelity of outdoor images are prone to degradation. Hazy images can be the cause of serious errors in many computer vision systems. Consequently, image haze removal has practical significance for real-world applications. In this study, we first analyze the inherent weaknesses of the atmospheric scattering model and propose an improvement to address those weaknesses. Then, we present a fast image haze removal algorithm based on the improved model. In our proposed method, the input image is partitioned into several scenes based on the haze thickness. Next, averaging and erosion operations calculate the rough scene luminance map in a scene-wise manner. We obtain the rough scene transmission map by maximizing the contrast in each scene and then develop a way to gently remove the haze using an adaptive method for adjusting scene transmission based on scene features. In addition, we propose a guided total variation model for edge optimization, so as to prevent from the block effect as well as to eliminate the negative effect from the wrong scene segmentation results. The experimental results demonstrate that our method is effective in solving a series of common problems, including uneven illuminance, overenhanced and oversaturated images, and so forth. Moreover, our method outperforms most current dehazing algorithms in terms of visual effects, universality, and processing speed.

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“…Some approaches make assumptions about the scene: constraints on image hue [14], on transmission [1], on image statistics [2]. In the state of art, other methods are dealing with dehazing [8,12,13]. However, they are generally not suitable to ADAS images [15] for two reasons: they are not real time or they do not take into account the context of the road (road surface, lane marking, and road signs).…”

Section: Introductionmentioning

confidence: 99%