A Local Contrast Enhancement Method for Mammograms in the Curvelet Domain

Author(s):  
Xue Qin ◽  
Yan Zhuangzhi ◽  
Li Xiaolan ◽  
Si Wen
2011 ◽  
Vol 383-390 ◽  
pp. 615-620
Author(s):  
Xue Yuan ◽  
Xue Ye Wei ◽  
Yong Duan Song

This paper presents a local contrast enhancement method, which is able to improve the detection performance of edge-based human detection. First, a neighborhood dependent local contrast enhancement method is used to enhance the images contrast. Next, the cascade AdaBoost classifier is used to discriminate between human and non-human. Experimental results show that the performance of our method is about 5% better than that of the conventional method.


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

1993 ◽  
Author(s):  
Mark A. Massie ◽  
James T. Woolaway II ◽  
Buu L. Huynh ◽  
Greg A. Johnson ◽  
Jon P. Curzan

2019 ◽  
Vol 11 (11) ◽  
pp. 1381 ◽  
Author(s):  
Chengwei Liu ◽  
Xiubao Sui ◽  
Xiaodong Kuang ◽  
Yuan Liu ◽  
Guohua Gu ◽  
...  

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.


2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Haidi Ibrahim ◽  
Seng Chun Hoo

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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