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 Adaptive Image Enhancement Using Entropy-Based Subhistogram Equalization

2018 ◽  
Vol 2018 ◽  
pp. 1-13 ◽  
Author(s):  
Liyun Zhuang ◽  
Yepeng Guan
Keyword(s):  
Image Enhancement ◽  
Dynamic Range ◽  
State Of The Art ◽  
Image Database ◽  
Input Image ◽  
Public Image ◽  
Contrast Enhanced ◽  
Quantitative Results ◽  
The Mean ◽  
Visual Assessments

A novel image enhancement approach called entropy-based adaptive subhistogram equalization (EASHE) is put forward in this paper. The proposed algorithm divides the histogram of input image into four segments based on the entropy value of the histogram, and the dynamic range of each subhistogram is adjusted. A novel algorithm to adjust the probability density function of the gray level is proposed, which can adaptively control the degree of image enhancement. Furthermore, the final contrast-enhanced image is obtained by equalizing each subhistogram independently. The proposed algorithm is compared with some state-of-the-art HE-based algorithms. The quantitative results for a public image database named CVG-UGR-Database are statistically analyzed. The quantitative and visual assessments show that the proposed algorithm outperforms most of the existing contrast-enhancement algorithms. The proposed method can make the contrast of image more effectively enhanced as well as the mean brightness and details well preserved.

Revisiting dynamic range and image enhancement ability of contemporary digital radiographic systems

2021 ◽  
Author(s):  
Luiz Eduardo Marinho ◽  
Luciano Augusto Cano Martins ◽  
Deborah Queiroz Freitas ◽  
Francisco Haiter-Neto ◽  
Matheus L. Oliveira
Keyword(s):  
Image Enhancement ◽  
Dynamic Range ◽  
Angular Coefficient ◽  
Image Brightness ◽  
File Formats ◽  
Contrast Enhanced ◽  
Phosphor Plate ◽  
The Mean ◽  
Post Hoc ◽  
The Relationship

Objectives: To assess the dynamic range and enhancement ability of radiographs acquired with contemporary digital systems. Methods: Five repeated periapical radiographs of human mandibles with an aluminium step-wedge were acquired using two sensor-based and three phosphor plate-based (PSP plate-based) systems and an X-ray unit at ten exposure times 0.020, 0.032, 0.063, 0.080, 0.100, 0.200, 0.320, 0.400, 0.500, and 0.630 s. All images had their brightness and contrast enhanced by two experienced oral and maxillofacial radiologists in consensus and were exported as both the original and enhanced file formats. Mean grey values were obtained from the aluminium steps and tabulated with their corresponding thicknesses for each exposure time, digital radiographic system, and file format. Images with saturated steps were excluded and the mean grey values from the remaining images were averaged to assess image brightness and the angular coefficient of the linear trendlines was generated from the relationship between mean grey values and their corresponding aluminium thicknesses to assess image contrast. Brightness and contrast values were compared using two-way ANOVA with post-hoc Tukey (α = 0.05). Results: PSP plate-based digital radiographic systems had a broader dynamic range. Longer exposure times produced original images with lower brightness and variable contrast (p < 0.05). Subjective enhancement significantly increased or reduced brightness and/or contrast in some systems (p < 0.05). Conclusions: Contemporary digital radiographic systems present different dynamic ranges and exposure-related brightness and contrast. Image enhancement may be a valuable tool at slightly suboptimal exposure times.

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 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 with contrast coefficients using wavelet based image fusion

2018 ◽  
Vol 7 (2.8) ◽  
pp. 432 ◽  
Author(s):  
P Pardhasaradhi ◽  
B T PMadhav ◽  
G Lakshmi Sindhuja ◽  
K Sai Sreeram ◽  
M Parvathi ◽  
...  
Keyword(s):  
Wavelet Transform ◽  
Image Fusion ◽  
Image Enhancement ◽  
Dynamic Range ◽  
Poor Quality ◽  
Information Loss ◽  
Image Brightness ◽  
Low Contrast ◽  
Different Temperatures

The future is mainly focused on image brightness and the capacity that required storing the image. The sharp images provide better information than the blur images. To overcome from the blurriness in the image, we use image enhancement techniques. Image fusion used to overcome information loss in the image. This paper is provided with image enhancement and fusion by applying wavelet transform technique. Wavelet transform is mainly used because due to its inherent property that is they are redundant and shift invariant. It transforms the image into different scales. Image enhancement will be decided based on the levels of transformation. Low contrast results from poor resolution, lack of dynamic range, wrong settings of sensor lens during acquisition and poor quality of cameras and sensors. To avoid the information loss there is an interesting solution that is for the pictures of the same image but focused on different regions. Then using image fusion concept, all images which are captured are combined to get a single image which contains the properties of both the source images. The image entropy is composed to determine the quality of the image. The paper shows the image fusion method for both multi-resolution and images captured at different temperatures.

scholarly journals IMAGE QUALITY IMPROVEMENT USING IMAGE PROCESSING METHOD IMAGE BRIGHTNESS CONTRAST AND IMAGE SHARPENING

2021 ◽  
Vol 1 (1) ◽  
pp. 6-12
Author(s):  
Fitri Rizani
Keyword(s):  
Image Processing ◽  
Quality Improvement ◽  
Image Quality ◽  
Processing Method ◽  
Image Contrast ◽  
Brightness Contrast ◽  
Image Brightness ◽  
Image Sharpening ◽  
Image Histogram ◽  
Image Quality Improvement

The ability of computers that are increasingly reliable in various fields, especially in helping the image processing sector through improving image quality, is very much felt so that the empowerment of computers at any time needs to be improved. Image quality improvement can be made with various techniques, including Image Quality Improvement with Image Brightness and Image Sharpening methods. The process begins with capturing the image and then continues with increasing the intensity of brightness, image contrast and sharpening. Image processing results are indicated by changes in the resulting image and changes in the image histogram

scholarly journals Research on an Improved Medical Image Enhancement Algorithm Based on P-M Model

2015 ◽  
Vol 9 (1) ◽  
pp. 209-213 ◽  
Author(s):  
Luo Aijing ◽  
Yin Jin
Keyword(s):  
Image Enhancement ◽  
Medical Image ◽  
Medical Images ◽  
Image Contrast ◽  
Image Brightness ◽  
Medical Image Enhancement ◽  
Enhancement Algorithm

Image enhancement can improve the detail of the image to achieve the purpose of the identification of the image. At present, the image enhancement is widely used in medical images, which can help doctor’s diagnosis. IEABPM (Image Enhancement Algorithm Based on P-M Model) is one of the most common image enhancement algorithms. However, it may cause the loss of the texture details and other features. To solve the problems, this paper proposes an IIEABPM (Improved Image Enhancement Algorithm Based on P-M Model). The simulation demonstrates that IIEABPM can effectively solve the problems of IEABPM, and improve image clarity, image contrast, and image brightness.

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.

A Modified Multi Scale Retinex with Color Restoration Algorithm for Automatic Enhancement of Landsat-5 Remote Sensing Image

2011 ◽  
Vol 341-342 ◽  
pp. 893-897
Author(s):  
Gui Zhou Wang ◽  
Guo Jin He
Keyword(s):  
Image Enhancement ◽  
Dynamic Range ◽  
Color Constancy ◽  
Human Perception ◽  
Histogram Equalization ◽  
Image Processing Algorithm ◽  
Multi Scale ◽  
Dynamic Range Compression ◽  
Restoration Algorithm ◽  
Color Restoration

The retinex is a human perception based image processing algorithm which provides color constancy and dynamic range compression. The multi scale retinex with color restoration (MSRCR) has shown itself to be a very versatile automatic image enhancement algorithm that simultaneously provides dynamic range compression, color constancy, and color rendition. But the MSRCR results suffer from lower global brightness and partial color distortion. In order to improve the MSRCR method, this paper presents a modified MSRCR algorithm to Landsat-5 image enhancement considering percent liner stretch and histogram adjustment. Finally, the effect of modified MSRCR method on Landsat-5 image enhancement is analyzed and the comparison with other color adjustment methods such as gamma correction and histogram equalization is reported in the experimental results.

Sign in / Sign up

Export Citation Format

Share Document