scholarly journals Adaptive Contrast Enhancement for Infrared Images Based on the Neighborhood Conditional Histogram

2019 ◽  
Vol 11 (11) ◽  
pp. 1381 ◽  
Author(s):  
Chengwei Liu ◽  
Xiubao Sui ◽  
Xiaodong Kuang ◽  
Yuan Liu ◽  
Guohua Gu ◽  
...  
Keyword(s):  
Contrast Enhancement ◽  
Noise Suppression ◽  
Mapping Function ◽  
Visual Quality ◽  
Histogram Equalization ◽  
The Other ◽  
Local Contrast ◽  
Infrared Images ◽  
Local Contrast Enhancement ◽  
Block Based

In this paper, an adaptive contrast enhancement method based on the neighborhood conditional histogram is proposed to improve the visual quality of thermal infrared images. Existing block-based local contrast enhancement methods usually suffer from the over-enhancement of smooth regions or the loss of some details. To address these drawbacks, we first introduce a neighborhood conditional histogram to adaptively enhance the contrast and avoid the over-enhancement caused by the original histogram. Then the clip-redistributed histogram of the contrast-limited adaptive histogram equalization (CLAHE) is replaced by the neighborhood conditional histogram. In addition, the local mapping function of each sub-block is updated based on the global mapping function to further eliminate the block artifacts. Lastly, the optimized local contrast enhancement process, which combines both global and local enhanced results is employed to obtain the desired enhanced result. Experiments are conducted to evaluate the performance of the proposed method and the other five methods are introduced as a comparison. Qualitative and quantitative evaluation results demonstrate that the proposed method outperforms the other block-based methods on local contrast enhancement, visual quality improvement, and noise suppression.

scholarly journals Optimized Contrast Enhancement for Infrared Images Based on Global and Local Histogram Specification

2019 ◽  
Vol 11 (7) ◽  
pp. 849 ◽  
Author(s):  
Chengwei Liu ◽  
Xiubao Sui ◽  
Xiaodong Kuang ◽  
Yuan Liu ◽  
Guohua Gu ◽  
...  
Keyword(s):  
Contrast Enhancement ◽  
Visual Quality ◽  
Local Contrast ◽  
Original Image ◽  
Infrared Images ◽  
Histogram Specification ◽  
Block Based ◽  
Global And Local ◽  
2D Histogram

In this paper, an optimized contrast enhancement method combining global and local enhancement results is proposed to improve the visual quality of infrared images. Global and local contrast enhancement methods have their merits and demerits, respectively. The proposed method utilizes the complementary characteristics of these two methods to achieve noticeable contrast enhancement without artifacts. In our proposed method, the 2D histogram, which contains both global and local gray level distribution characteristics of the original image, is computed first. Then, based on the 2D histogram, the global and local enhanced results are obtained by applying histogram specification globally and locally. Lastly, the enhanced result is computed by solving an optimization equation subjected to global and local constraints. The pixel-wise regularization parameters for the optimization equation are adaptively determined based on the edge information of the original image. Thus, the proposed method is able to enhance the local contrast while preserving the naturalness of the original image. Qualitative and quantitative evaluation results demonstrate that the proposed method outperforms the block-based methods for improving the visual quality of infrared images.

scholarly journals Local Contrast Enhancement Using Intuitionistic Fuzzy Sets Optimized By Artificial Bee Colony Algorithm

2017 ◽  
Vol 8 (1) ◽  
pp. 7
Author(s):  
Daniel M. Wonohadidjojo
Keyword(s):  
Fuzzy Sets ◽  
Contrast Enhancement ◽  
Artificial Bee Colony ◽  
Histogram Equalization ◽  
Intuitionistic Fuzzy Sets ◽  
Local Contrast ◽  
Intuitionistic Fuzzy ◽  
Bee Colony ◽  
Local Contrast Enhancement ◽  
Equalization Method

The article presented the enhancement method of cells images. The first method used in the local contrast enhancement was Intuitionistic Fuzzy Sets (IFS). The proposed method is the IFS optimized by Artificial Bee Colony (ABC) algorithm. The ABC was used to optimize the membership function parameter of IFS. To measure the image quality, Image Enhancement Metric (IEM)was applied. The results of local contrast enhancement using both methods were compared with the results using histogram equalization method. The tests were conducted using two MDCK cell images. The results of local contrast enhancement using both methods were evaluated by observing the enhanced images and IEM values. The results show that the methods outperform the histogram equalization method. Furthermore, the method using IFSABC is better than the IFS method.

scholarly journals Clinical evaluation of ‘local contrast enhancement’ for oral fluorescein angiograms

Eye ◽  
2000 ◽  
Vol 14 (3) ◽  
pp. 318-318 ◽  
Author(s):  
Richard S B Newsom ◽  
Chanjira Sinthanayothin ◽  
James Boyce ◽  
Anthony G Casswell ◽  
Tom H Williamson
Keyword(s):  
Contrast Enhancement ◽  
Clinical Evaluation ◽  
Local Contrast ◽  
Local Contrast Enhancement

Neuromorphic infrared focal plane performs on-plane local contrast enhancement, spatial, and temporal filtering

1993 ◽  
Author(s):  
Mark A. Massie ◽  
James T. Woolaway II ◽  
Buu L. Huynh ◽  
Greg A. Johnson ◽  
Jon P. Curzan
Keyword(s):  
Contrast Enhancement ◽  
Focal Plane ◽  
Local Contrast ◽  
Temporal Filtering ◽  
Local Contrast Enhancement

scholarly journals Local Contrast Enhancement Utilizing Bidirectional Switching Equalization of Separated and Clipped Subhistograms

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Haidi Ibrahim ◽  
Seng Chun Hoo
Keyword(s):  
Contrast Enhancement ◽  
Histogram Equalization ◽  
Processing Technique ◽  
Image Contrast ◽  
Local Contrast ◽  
Image Contrast Enhancement ◽  
Enhancement Method ◽  
Equalization Technique ◽  
Histogram Bins ◽  
Almost All

Digital image contrast enhancement methods that are based on histogram equalization technique are still useful for the use in consumer electronic products due to their simple implementation. However, almost all the suggested enhancement methods are using global processing technique, which does not emphasize local contents. Therefore, this paper proposes a new local image contrast enhancement method, based on histogram equalization technique, which not only enhances the contrast, but also increases the sharpness of the image. Besides, this method is also able to preserve the mean brightness of the image. In order to limit the noise amplification, this newly proposed method utilizes local mean-separation, and clipped histogram bins methodologies. Based on nine test color images and the benchmark with other three histogram equalization based methods, the proposed technique shows the best overall performance.

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