Image Enhancement Based on Equal Area Dualistic Sub-image and Non-parametric Modified Histogram Equalization Method

This piece of work investigates the application of histogram equalization method to clinical images for noise removal and efficient image enhancement without any information loss. Computed tomographic (CT) images of the abdomen bearing liver tumour are kept under study. Liver exhibits heterogeneous combination of intensities which makes it a challenging task to enhance the liver tumour embedded in the image. Distortion occurs due to the presence of quantum noise in the CT scans and important information of the image is suppressed. The methodology adopted in this paper comprises of two stages. Initially pixel based intensity transformation is adopted for de-noising the background of the image by the selection of appropriate threshold levels. The resultant image gives a noise free background and the foreground features are enhanced. In the next stage histogram equalization filters are applied to the transformed image. The equalization method which gives uniform image enhancement with lesser mean square error (MSE) and increased peak signal to noise ratio (PSNR) is supposed to be an effective method for efficient enhancement of the images. This study deals with the application of histogram equalization methods to CT images which can aid the radiologists for better visualization and diagnosis of the disease.

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Brightness preserving and non-parametric modified bi-histogram equalization for image enhancement

2016 12th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD) ◽

10.1109/fskd.2016.7603464 ◽

2016 ◽

Cited By ~ 1

Author(s):

Zhijun Yao ◽

Zhongyuan Lai ◽

Chun Wang

Keyword(s):

Image Enhancement ◽

Histogram Equalization ◽

Non Parametric

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The Eye Retinal Blood Vessel Bonding By Image Processing

International Journal of Recent Technology and Engineering - 2 ◽

10.35940/ijrte.c5801.098319 ◽

2019 ◽

Vol 8 (3) ◽

pp. 6848-6851

Keyword(s):

Image Processing ◽

Blood Vessel ◽

Image Enhancement ◽

Histogram Equalization ◽

Retinal Blood Vessel ◽

Contrast Method ◽

Equalization Method

The method Histogram equalization is common in image enhancement. Using histogram to contrast the entire image and reduce noise .but we are using histogram equalization method remove the noise on entire image. But in some applications this is not suitable. Using contrast method we perform on small regions where our needed. The clip and block side method we will enhance the image. The contrast should be enhanced in this paper. Here we are used algorithm based on algorithm we should find the quality of the vessel bonding

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Image Pixel Contrast Enhancement Using Enhanced Multi Histogram Equalization Method

Ingénierie des systèmes d information ◽

10.18280/isi.260110 ◽

2021 ◽

Vol 26 (1) ◽

pp. 95-101

Author(s):

Gutta Srinivasa Rao ◽

Atluri Srikrishna

Keyword(s):

Contrast Enhancement ◽

Image Enhancement ◽

Satellite Image ◽

Histogram Equalization ◽

Image Contrast ◽

The Poor ◽

Image Contrast Enhancement ◽

Poor Lighting ◽

Equalization Method ◽

Image Datasets

Image Enhancement methods produce various sorts of problems, for example, unnatural impacts, over-improvement, and these downsides become increasingly unmistakable in improving dull Images. Histogram Equalization (HE) method is a straightforward and generally utilized Image contrast enhancement procedure. The fundamental task of HE is it changes the contrast of the Image. To perform this task, different HE techniques have been proposed. These techniques protect the brightness or contrast on the final Image that doesn't have a characteristic look. To overcome the drawbacks of HE, Enhanced Multi Histogram Equalization (EMHE) technique is proposed, which divide the Image into a few sub images and again these sub images are divided into sub-images, and traditional HE strategy is applied to each sub Image for getting better results. The improvement is brought about by repetitive data present in sub-pixel moves between relating Lightroom (LR) Images of a similar scene. The principal phase of the development incorporates Image enrollment of LR Images utilizing known parameters and geo-referencing methods for manufactured and genuine information individually. The proposed development of M-HE Images has been assessed on the LR Images obtained from satellite Image datasets to exhibit the clarity of the images by enhancing the contrast on the poor lighting images.

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