Linear Histogram Adjustment for Image Enhancement

2020 ◽  
Vol 2020 (1) ◽  
pp. 65-68
Author(s):  
Jake McVey
Keyword(s):  
Contrast Enhancement ◽  
Image Enhancement ◽  
Input Image ◽  
Surprising Result ◽  
Image Histogram ◽  
Non Linear ◽  
Linear Transform

Tone curves are one of the simplest techniques for image enhancement. Specified as a function, a tone curve is a transformation that maps pixel levels of an input image to new output levels. Tone curves are the basis of many contrast enhancement algorithms, including Contrast Limited Histogram Equalisation (CLHE), which derives a tone curve from a modification of the image histogram. While these methods can provide good enhancement, they are generally non-linear. In this paper we show the surprising result that a tone curve generated by the non-linear CLHE method (and HE) can be calculated by applying a linear transform to the histogram of the input image. Experiments validate our method.

scholarly journals Contrast and Synthetic Multiexposure Fusion for Image Enhancement

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Marwan Ali Albahar
Keyword(s):  
Image Quality ◽  
Contrast Enhancement ◽  
Image Enhancement ◽  
State Of The Art ◽  
Input Image ◽  
Synthetic Image ◽  
Improve Image Quality ◽  
Exposure Fusion ◽  
Novel Technique ◽  
Lighting Conditions

Many hardware and software advancements have been made to improve image quality in smartphones, but unsuitable lighting conditions are still a significant impediment to image quality. To counter this problem, we present an image enhancement pipeline comprising synthetic multi-image exposure fusion and contrast enhancement robust to different lighting conditions. In this paper, we propose a novel technique of generating synthetic multi-exposure images by applying gamma correction to an input image using different values according to its luminosity for generating multiple intermediate images, which are then transformed into a final synthetic image by applying contrast enhancement. We observed that our proposed contrast enhancement technique focuses on specific regions of an image resulting in varying exposure, colors, and details for generating synthetic images. Visual and statistical analysis shows that our method performs better in various lighting scenarios and achieves better statistical naturalness and discrete entropy scores than state-of-the-art methods.

scholarly journals Retinal Image Enhancement using Ordering Gap Adjustment and Brightness Specification

2020 ◽  
Vol 14 ◽  
Keyword(s):  
Image Enhancement ◽  
Retinal Image ◽  
Dynamic Range ◽  
Input Image ◽  
Image Contrast ◽  
Effective Algorithm ◽  
Problem Domain ◽  
Image Brightness ◽  
Image Histogram ◽  
Matlab Program

Color retinal image enhancement plays an important role in improving an image quality suited for reliable diagnosis. For this problem domain, a simple and effective algorithm for image contrast and color balance enhancement namely Ordering Gap Adjustment and Brightness Specification (OGABS) was proposed. The OGABS algorithm first constructs a specified histogram by adjusting the gap of the input image histogram ordering by its probability density function under gap limiter and Hubbard’s dynamic range specifications. Then, the specified histograms are targets to redistribute the intensity values of the input image based on histogram matching. Finally, color balance is improved by specifying the image brightness based on Hubbard’s brightness specification. The OGABS algorithm is implemented by the MATLAB program and the performance of our algorithm has been evaluated against data from STARE and DiaretDB0 datasets. The results obtained show that our algorithm enhances the image contrast and creates a good color balance in a pleasing natural appearance with a standard color of lesions.

scholarly journals An Effective Image Contrast Enhancement Method Using Global Histogram Equalization

2010 ◽  
Vol 3 (1) ◽  
pp. 43 ◽  
Author(s):  
M. A. Yousuf ◽  
M. R. H. Rakib
Keyword(s):  
Contrast Enhancement ◽  
Image Enhancement ◽  
Histogram Equalization ◽  
Input Image ◽  
Image Contrast ◽  
Image Contrast Enhancement ◽  
Enhancement Method ◽  
Calculated Probability ◽  
The Mean ◽  
Processing Techniques

Image enhancement is one of the most important issues in low-level image processing. Histograms are the basis for numerous spatial domain processing techniques. In this paper, we present a simple and effective method for image contrast enhancement based on global histogram equalization. In this method, at first input image is normalized by making the minimum gray level value to 0.  Then the probability of each grey level is calculated from the available ROI grey levels. Finally, histogram equalization is performed on the input image based on the calculated probability density (or distribution) function. As a result, the mean brightness of the input image does not change significantly by the histogram equalization. Additionally, noise is prevented from being greatly amplified. Experimental results on medical images demonstrate that the proposed method can enhance the images effectively. The result is also compared with the result of image enhancement technique using local statistics.Keywords: Histogram equalization; Global histogram equalization; Image enhancement; Local statistics.© 2011 JSR Publications. ISSN: 2070-0237 (Print); 2070-0245 (Online). All rights reserved.doi:10.3329/jsr.v3i1.5299                J. Sci. Res. 3 (1), 43-50 (2011)

scholarly journals Image Enhancement by Histogram Specification Using Multiple Target Images

2011 ◽  
pp. 29-37
Author(s):  
Pavithra P ◽  
Ramyashree N ◽  
Shruthi T.V ◽  
Dr. Jharna Majumdar
Keyword(s):  
High Resolution ◽  
Image Enhancement ◽  
Input Image ◽  
Multiple Target ◽  
Target Image ◽  
Image Histogram ◽  
Histogram Specification ◽  
High Resolution Images

Shape and characteristics of the histogram plays a major role in finding the quality of an image. Histogram Specification is an image enhancement technique, where the histogram of the input image is transformed to a pre-specified histogram derived from a high resolution image, called target image. In this paper, the classical histogram specification technique is extended by using a target image which is obtained by fusing multiple high resolution images. A set of Quality Metrics were identified to assess the quality of the output enhanced image. The paper addresses the following issues: a) Effect of varying the number of target images on the quality of the output enhanced image b) Role of using different methods of fusion on the quality of the output enhanced image c) Category of the target image on the quality of the output enhanced image. If the input image is from a forest, whether in order to obtain an enhanced image, all target images has to be selected from the forest category d) Effect of preprocessing of target image on the quality of the output enhanced image.

Non-Linear Masking based Contrast Enhancement via Illumination Estimation

2018 ◽  
Vol 2018 (13) ◽  
pp. 389-1-389-5
Author(s):  
Soonyoung Hong ◽  
Minsub Kim ◽  
Moon Gi Kang
Keyword(s):  
Contrast Enhancement ◽  
Illumination Estimation ◽  
Non Linear

Color correction and adaptive contrast enhancement for underwater image enhancement

2021 ◽  
Vol 91 ◽  
pp. 106981
Author(s):  
Weidong Zhang ◽  
Xipeng Pan ◽  
Xiwang Xie ◽  
Lingqiao Li ◽  
Zimin Wang ◽  
...  
Keyword(s):  
Contrast Enhancement ◽  
Image Enhancement ◽  
Color Correction ◽  
Underwater Image

Application of non-linear transform coding to image processing

2012 ◽  
Author(s):  
Jens Hocke ◽  
Erhardt Barth ◽  
Thomas Martinetz
Keyword(s):  
Image Processing ◽  
Transform Coding ◽  
Non Linear ◽  
Linear Transform
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