A novel method for design of gray-level transformation functions for image contrast improvement based on the histogram equalization technique

2013 ◽  
Vol 96 (7) ◽  
pp. 57-66 ◽  
Author(s):  
Kota Murahira ◽  
Takashi Kawakami ◽  
Akira Taguchi
Keyword(s):  
Histogram Equalization ◽  
Image Contrast ◽  
Gray Level ◽  
Novel Method ◽  
Equalization Technique ◽  
Transformation Functions

scholarly journals Local Contrast Enhancement Utilizing Bidirectional Switching Equalization of Separated and Clipped Subhistograms

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Haidi Ibrahim ◽  
Seng Chun Hoo
Keyword(s):  
Contrast Enhancement ◽  
Histogram Equalization ◽  
Processing Technique ◽  
Image Contrast ◽  
Local Contrast ◽  
Image Contrast Enhancement ◽  
Enhancement Method ◽  
Equalization Technique ◽  
Histogram Bins ◽  
Almost All

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.

scholarly journals Improving radiographic image contrast using multi layers of histogram equalization technique

2021 ◽  
Vol 10 (1) ◽  
pp. 151
Author(s):  
Farah F. Alkhalid ◽  
Ahmed Mudher Hasan ◽  
Ahmed A. Alhamady
Keyword(s):  
Decision Analysis ◽  
Nerve Root ◽  
Histogram Equalization ◽  
Image Contrast ◽  
High Contrast ◽  
Radiographic Image ◽  
X Ray ◽  
Adaptive Histogram Equalization ◽  
Suggested Technique ◽  
Equalization Technique

<span id="docs-internal-guid-43432eef-7fff-9949-6deb-865191ff0740"><span>Usually, X-ray image has distortion in many parts because it is focusing on bones rather than other, However, when dentist needs to make decision analysis, he does that by using X-ray and many opinions can be judged by looking closely on it like (inflammation, infection, tooth nerve, root of the tooth…). This paper proposes on new suggested technique by applying multilayers of histogram equalization (HE) and contrast limited adaptive histogram equalization (CLAHE) in order to make high contrast of X-ray, this technique provides very satisfied results and smooth intensity which leads to high clear X-ray image, by using Python3 and OpenCV.</span></span>

scholarly journals Crop Analysis Using Image Processing

2020 ◽  
Vol 4 (3) ◽  
pp. 9-15
Author(s):  
Srujana V ◽  
Chaithanya P ◽  
Ramesh B ◽  
Manoranjan S ◽  
Mahesh V
Keyword(s):  
Histogram Equalization ◽  
Gray Level ◽  
Contrast Level ◽  
Pass Filter ◽  
Low Pass Filter ◽  
Modern Agriculture ◽  
Low Pass ◽  
Gray Level Image ◽  
Equalization Technique ◽  
Binary Features

To detect the uniqueness and quantities of agriculture product images a new method is proposed using MATLAB software .In this paper we propose a method to increase the contrast level of a image with exponential low pass filter and histogram equalization technique. Next by using region props function we extract the binary features of the image, and then we calculated the number of targets in gray level image. This method can be easily applied in modern agriculture.

Image Quality Enhancing by Efficient Histogram Equalization

2014 ◽  
Vol 2 (2) ◽  
pp. 47-58
Author(s):  
Ismail Sh. Baqer
Keyword(s):  
Image Quality ◽  
Contrast Enhancement ◽  
Mean Squared Error ◽  
Signal To Noise Ratio ◽  
Histogram Equalization ◽  
Gray Level ◽  
Signal To Noise ◽  
Squared Error ◽  
Noise Ratio

A two Level Image Quality enhancement is proposed in this paper. In the first level, Dualistic Sub-Image Histogram Equalization DSIHE method decomposes the original image into two sub-images based on median of original images. The second level deals with spikes shaped noise that may appear in the image after processing. We presents three methods of image enhancement GHE, LHE and proposed DSIHE that improve the visual quality of images. A comparative calculations is being carried out on above mentioned techniques to examine objective and subjective image quality parameters e.g. Peak Signal-to-Noise Ratio PSNR values, entropy H and mean squared error MSE to measure the quality of gray scale enhanced images. For handling gray-level images, convenient Histogram Equalization methods e.g. GHE and LHE tend to change the mean brightness of an image to middle level of the gray-level range limiting their appropriateness for contrast enhancement in consumer electronics such as TV monitors. The DSIHE methods seem to overcome this disadvantage as they tend to preserve both, the brightness and contrast enhancement. Experimental results show that the proposed technique gives better results in terms of Discrete Entropy, Signal to Noise ratio and Mean Squared Error values than the Global and Local histogram-based equalization methods

scholarly journals Detection and Classification of Cancer from Microscopic Biopsy Images Using Clinically Significant and Biologically Interpretable Features

2015 ◽  
Vol 2015 ◽  
pp. 1-14 ◽  
Author(s):  
Rajesh Kumar ◽  
Rajeev Srivastava ◽  
Subodh Srivastava
Keyword(s):  
Texture Features ◽  
Histogram Equalization ◽  
Gray Level ◽  
Segmentation Methods ◽  
Segmented Images ◽  
Clinically Significant ◽  
Classification Of Images ◽  
Nearest Neighborhood ◽  
Neighborhood Method

A framework for automated detection and classification of cancer from microscopic biopsy images using clinically significant and biologically interpretable features is proposed and examined. The various stages involved in the proposed methodology include enhancement of microscopic images, segmentation of background cells, features extraction, and finally the classification. An appropriate and efficient method is employed in each of the design steps of the proposed framework after making a comparative analysis of commonly used method in each category. For highlighting the details of the tissue and structures, the contrast limited adaptive histogram equalization approach is used. For the segmentation of background cells, k-means segmentation algorithm is used because it performs better in comparison to other commonly used segmentation methods. In feature extraction phase, it is proposed to extract various biologically interpretable and clinically significant shapes as well as morphology based features from the segmented images. These include gray level texture features, color based features, color gray level texture features, Law’s Texture Energy based features, Tamura’s features, and wavelet features. Finally, the K-nearest neighborhood method is used for classification of images into normal and cancerous categories because it is performing better in comparison to other commonly used methods for this application. The performance of the proposed framework is evaluated using well-known parameters for four fundamental tissues (connective, epithelial, muscular, and nervous) of randomly selected 1000 microscopic biopsy images.

scholarly journals Enhancement of Satellite Imagesusing Image Sharpening Technique

2019 ◽  
Vol 8 (6S2) ◽  
pp. 351-354
Keyword(s):  
Image Processing ◽  
Color Image ◽  
Histogram Equalization ◽  
Grayscale Image ◽  
Original Image ◽  
High Quality ◽  
Wide Range ◽  
Equalization Technique ◽  
The Difference

In many image processing applications, a wide range of image enhancement techniques are being proposed. Many of these techniques demanda lot of critical and advance steps, but the resultingimage perception is not satisfactory. This paper proposes a novel sharpening method which is being experimented with additional steps. In the first step, the color image is transformed into grayscale image, then edge detection process is applied using Laplacian technique. Then deduct this image from the original image. The resulting image is as expected; After performing the enhancement process,the high quality of the image can be indicated using the Tenengrad criterion. The resulting image manifested the difference in certain areas, the dimension and the depth as well. Histogram equalization technique can also be applied to change the images color.

scholarly journals Investigating the role of global histogram equalization technique for 99mTechnetium-Methylene diphosphonate bone scan image enhancement

2017 ◽  
Vol 32 (4) ◽  
pp. 283 ◽  
Author(s):  
AnilKumar Pandey ◽  
ParamDev Sharma ◽  
Pankaj Dheer ◽  
GirishKumar Parida ◽  
Harish Goyal ◽  
...  
Keyword(s):  
Image Enhancement ◽  
Bone Scan ◽  
Histogram Equalization ◽  
Methylene Diphosphonate ◽  
Equalization Technique
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