Local Contrast Regularized Contrast Limited Adaptive Histogram Equalization Using Tree Seed Algorithm—An Aid for Mammogram Images Enhancement

Mammography is an operative procedure for early detection of cancer present in breast. However, the pathological changes of the breast are difficult to interpret from low contrast mammograms. This research proposes a method to enhance the contrast of the mammogram that uses Non-subsampled contourlet transform (NSCT) based edge information. Instead of a directional filter bank in the conventional NSCT structure, this paper uses multiscale non-separable edge filters. These edge filters outputs intrinsic edge structure information based on simplified hyperbolic tangent function applied with two polarized schemes. This edge information further used to improve the local contrast. Adaptive histogram equalization (AHE) also used to increase the overall contrast of mammogram. Improved detection of microcalcification (MC) from enhanced mammogram images shows the success of this algorithm. This method has better enhancement measure (EME) than AHE and unsharp based mammogram enhancement method.

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CONTRAST ENHANCED SUPERPIXEL BASED SEGMENTATION OF RETINAL IMAGES

10.36227/techrxiv.12254240 ◽

2020 ◽

Author(s):

Abhisha Mano

Keyword(s):

Blood Vessels ◽

Histogram Equalization ◽

Retinal Images ◽

Local Contrast ◽

Adaptive Histogram Equalization ◽

Contrast Enhanced

Contrast limited adaptive histogram equalization is applied to obtain local contrast enhancement.retinal blood vessels are segmented by minimum spanning superpixel tree detector.<br>

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CONTRAST ENHANCED SUPERPIXEL BASED SEGMENTATION OF RETINAL IMAGES

10.36227/techrxiv.12254240.v2 ◽

2020 ◽

Author(s):

Abhisha Mano

Keyword(s):

Blood Vessels ◽

Histogram Equalization ◽

Retinal Images ◽

Local Contrast ◽

Adaptive Histogram Equalization ◽

Contrast Enhanced

Contrast limited adaptive histogram equalization is applied to obtain local contrast enhancement.retinal blood vessels are segmented by minimum spanning superpixel tree detector.<br>

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Low-Light Image Enhancement Based on Quasi-Symmetric Correction Functions by Fusion

Symmetry ◽

10.3390/sym12091561 ◽

2020 ◽

Vol 12 (9) ◽

pp. 1561

Author(s):

Changli Li ◽

Shiqiang Tang ◽

Jingwen Yan ◽

Teng Zhou

Keyword(s):

Image Enhancement ◽

State Of The Art ◽

Histogram Equalization ◽

Local Contrast ◽

Low Light ◽

Adaptive Histogram Equalization ◽

Light Image ◽

Image Capturing ◽

Enhancement Method ◽

Image Details

Sometimes it is very difficult to obtain high-quality images because of the limitations of image-capturing devices and the environment. Gamma correction (GC) is widely used for image enhancement. However, traditional GC perhaps cannot preserve image details and may even reduce local contrast within high-illuminance regions. Therefore, we first define two couples of quasi-symmetric correction functions (QCFs) to solve these problems. Moreover, we propose a novel low-light image enhancement method based on proposed QCFs by fusion, which combines a globally-enhanced image by QCFs and a locally-enhanced image by contrast-limited adaptive histogram equalization (CLAHE). A large number of experimental results showed that our method could significantly enhance the detail and improve the contrast of low-light images. Our method also has a better performance than other state-of-the-art methods in both subjective and objective assessments.

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CONTRAST ENHANCED SUPERPIXEL BASED SEGMENTATION OF RETINAL IMAGES

10.36227/techrxiv.12254240.v1 ◽

2020 ◽

Author(s):

Abhisha Mano

Keyword(s):

Blood Vessels ◽

Histogram Equalization ◽

Retinal Images ◽

Local Contrast ◽

Adaptive Histogram Equalization ◽

Contrast Enhanced

Contrast limited adaptive histogram equalization is applied to obtain local contrast enhancement.retinal blood vessels are segmented by minimum spanning superpixel tree detector.<br>

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Spatially Adaptive Histogram Equalization for Single Image-Based Ghost-free High Dynamic Range Imaging

TECHART Journal of Arts and Imaging Science ◽

10.15323/jais.2014.02.1.1.55 ◽

2014 ◽

Author(s):

Jaehyun Im

Keyword(s):

Dynamic Range ◽

High Dynamic Range ◽

Histogram Equalization ◽

High Dynamic Range Imaging ◽

Single Image ◽

Range Imaging ◽

Adaptive Histogram Equalization ◽

Spatially Adaptive ◽

High Dynamic

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PENERAPAN METODE CLAHE UNTUK MEMPERJELAS OBJEK PANTULAN KACA PADA CITRA DIGITAL

KOMIK (Konferensi Nasional Teknologi Informasi dan Komputer) ◽

10.30865/komik.v3i1.1563 ◽

2019 ◽

Vol 3 (1) ◽

Author(s):

Sulharmi Irawan ◽

Yasir Hasan ◽

Kennedi Tampubolon

Keyword(s):

Image Processing ◽

Computer Graphics ◽

Digital Image ◽

Histogram Equalization ◽

Image Objects ◽

Adaptive Histogram Equalization ◽

Image Displays ◽

Reflection Image

Glass reflection image displays unclear or suboptimal visuals, such as overlapping images that blend with overlapping displays, so objects in images that have information and should be able to be processed for advanced research in the field of image processing or computer graphics do not give the impression so that research can be done. Improvement of overlapping images can be separated by displaying one of the image objects, the method that can be used is the Contras Limited Adaptive Histogram Equalization (CLAHE) method. CLAHE can improve the color and appearance of objects that are not clear on the image. Images that experience cases such as glass reflection images can be increased in contrast values to separate or accentuate one of the objects contained in the image using the Contrast Limited Adaptive Histogram Equalization (CLAHE) method.Keywords: Digital Image, Glass Reflection, Contrast, CLAHE, YIQ.

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