Fog Removal from Video Sequences Using Contrast Limited Adaptive Histogram Equalization

Xu, Zhiyuan; Liu, Xiaoming; Chen, Xiaonan

doi:10.1109/cise.2009.5366207

Cited by 67 publications

(33 citation statements)

References 4 publications

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“…Pixels in the borders of the image outside of the sample pixels need to be processed specially. The neighboring tiles were combined using bilinear interpolation and the gray scale values were altered according to the modified histograms [33].…”

Section: Contrast Limited Adaptive Histogram Equalization (Clahe)mentioning

confidence: 99%

Multistage Approach for Simple Kidney Cysts Segmentation in CT Images

Georgieva

Draganov

2016

New Approaches in Intelligent Image Analysis

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In the chapter is presented a multistage approach for segmentation of medical objects in Computed Tomography (CT) images. Noise reduction with consecutive applied median filter and wavelet shrinkage packet decomposition, and contrast enhancement based on Contrast limited adaptive histogram equalization (CLAHE) are applied in preprocessing stage. As a next step is used a combination of 2 basic methods for image segmentation such as split and merge algorithm, following by color based K-mean clustering. For refining the boundaries of the detected objects additional texture analysis is introduced based on limited Haralick's feature set and morphological filters. Due to the diminished number of components for the feature vectors the speed of the segmentation stage is higher in comparison with the full feature set. Some experimental results are presented, obtained by computer simulation in the MATLAB environment. The experimental results give detailed information about detected simple renal cysts and their boundaries in axial plane of CT images which are presented in native, arterial and venous phases. The proposed approach can be used in real time for precise diagnosis or in monitoring the disease progression.

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Section: Contrast Limited Adaptive Histogram Equalization (Clahe)mentioning

confidence: 99%

Multistage Approach for Simple Kidney Cysts Segmentation in CT Images

Georgieva

Draganov

2016

New Approaches in Intelligent Image Analysis

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“…The image restoration method covers an intrinsic luminance of an object using additional information or prior imposition [5]- [11]. The image enhancement method using such as human vision system (HVS) without scene information tends to increase dynamic range and contrast of image degraded by fog or haze [12]- [19].…”

Section: Introductionmentioning

confidence: 99%

“…In the image enhancement researches, histogram equalization approach [12] and Retinex theory [13]- [19] have been studied. Using histogram, the result seems to be more affected by noise and has two problems.…”

Section: Introductionmentioning

confidence: 99%

Visibility Enhancement Based Real -- Time Retinex for Diverse Environments

Kim

Lee

2012

2012 Eighth International Conference on Signal Image Technology and Internet Based Systems

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This paper proposes a modified structure of Retinex algorithm for the image enhancement method. The proposed algorithm performs robustly and stably on various environments, particularly in bad weather and night vision. Especially, Retinex algorithm tends to show outstanding results as compared with other approaches, and also shows advantages at night vision. However, the Retinex algorithm has problems such as the halo effect and the color distortion. In this paper, the proposed algorithm has a simple idea to remove above problems that combines a reflectance and a luminance image with some constant factors respectively. Since the result includes the luminance component, it works on diverse environment such as not only foggy image but also night vision image. Furthermore, this algorithm has proper structure for real time processing. Results on a variety of images show the effectiveness of this approach.

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“…In Multi-frame averaging method, motion blur and trailing are common flaws. In paper [17] a threshold based algorithm is presented to mitigate these flaws. Although it rectifies the flaws, the problem of the algorithm is that it uses a fixed threshold for all pixels.…”

Section: Background Extractionmentioning

confidence: 99%

“…Among this, one of the most classic adaptive local histogram equalization methods is the contrast limited adaptive histogram equalization. Recent works [15][16][17] address on CLAHE which exploits the local image statistics. This method is also simple and efficient to implement.…”

Section: Introductionmentioning

confidence: 99%

A Novel Method for the Contrast Enhancement of Fog Degraded Video Sequences

Ramya¹,

Rani²

2012

IJCA

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Videos taken under fog suffer from degradation such as severe contrast loss. Unfortunately, that effect of fog cannot be overcome by simple image processing techniques. In this paper, a novel method for the contrast enhancement of foggy video sequences is proposed based on the Contrast Limited Adaptive Histogram Equalization (CLAHE), which limits the intensity of each pixel to user determined maximum. Thus, it mitigates the degradation due to fog and improves the visibility of the video signal. Initially, the background and foreground images are extracted from the video sequence. Then, the background and foreground images are separately defogged by applying CLAHE. The defogged background and foreground images are fused into the new frame. Finally, the defogged video sequence is obtained. The experimental results show that the proposed method is more effective than the traditional method. Performance of the proposed method is also analyzed with contrast improvement index (CI) and Tenengrad criterion (TEN).

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Fog Removal from Video Sequences Using Contrast Limited Adaptive Histogram Equalization

Cited by 67 publications

References 4 publications

Multistage Approach for Simple Kidney Cysts Segmentation in CT Images

Multistage Approach for Simple Kidney Cysts Segmentation in CT Images

Visibility Enhancement Based Real -- Time Retinex for Diverse Environments

A Novel Method for the Contrast Enhancement of Fog Degraded Video Sequences

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