scholarly journals Reduction based Histogram Equalization Technique for Image Enhancement

2019 ◽  
Vol 8 (11S) ◽  
pp. 1268-1271
Keyword(s):  
Image Processing ◽  
Image Enhancement ◽  
Histogram Equalization ◽  
Input Image ◽  
Subjective Quality ◽  
Lower Intensity ◽  
Equalization Technique ◽  
Human Interpretation

Here the proposed scheme mainly emphasizes the procedure of histogram equalization of images in more efficient way. Histogram equalization is required for image enhancement. Histogram spreads or flattens the histogram of an image and due to this the pixels with lower intensity values appear darker and the pixels with higher intensity values appear lighter. So the contrast of the input image is improved. For human interpretation various techniques of image enhancement have been widely used in different applications areas of image processing as the subjective quality of images is mainly important

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 A Review on Deep Image Contrast Enhancement

2020 ◽  
Vol 6 (1) ◽  
pp. 4
Author(s):  
Puspad Kumar Sharma ◽  
Nitesh Gupta ◽  
Anurag Shrivastava
Keyword(s):  
Image Processing ◽  
Deep Learning ◽  
Image Enhancement ◽  
Research Work ◽  
Atmospheric Condition ◽  
Input Image ◽  
Learning Approaches ◽  
Sensing Applications ◽  
Image Contrast Enhancement

In image processing applications, one of the main preprocessing phases is image enhancement that is used to produce high quality image or enhanced image than the original input image. These enhanced images can be used in many applications such as remote sensing applications, geo-satellite images, etc. The quality of an image is affected due to several conditions such as by poor illumination, atmospheric condition, wrong lens aperture setting of the camera, noise, etc [2]. So, such degraded/low exposure images are needed to be enhanced by increasing the brightness as well as its contrast and this can be possible by the method of image enhancement. In this research work different image enhancement techniques are discussed and reviewed with their results. The aim of this study is to determine the application of deep learning approaches that have been used for image enhancement. Deep learning is a machine learning approach which is currently revolutionizing a number of disciplines including image processing and computer vision. This paper will attempt to apply deep learning to image filtering, specifically low-light image enhancement. The review given in this paper is quite efficient for future researchers to overcome problems that helps in designing efficient algorithm which enhances quality of the image.

scholarly journals The Eye Retinal Blood Vessel Bonding By Image Processing

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

scholarly journals Comparative study of image enhancement techniques using histogram equalization on degraded images

2018 ◽  
Vol 7 (2.8) ◽  
pp. 468 ◽  
Author(s):  
Shalika Arora ◽  
Megha Agarwal ◽  
Veepin Kumar ◽  
Divya Gupta
Keyword(s):  
Image Processing ◽  
Comparative Study ◽  
Image Enhancement ◽  
Digital Image Processing ◽  
Digital Image ◽  
Histogram Equalization ◽  
Vital Role ◽  
Enhancement Technique ◽  
Degraded Images

Image Enhancement technique plays a vital role in digital image processing for making an image to be useful for various applications. This technique is used to improve the quality of degraded images.Usually, the degradation is not evenly spread throughout the image, but most of the time it varies from region to region. Our aim is to first identify the region where enhancement is required and improve that region without disturbing its neighbourhood which does not require any improvement. 

scholarly journals Contrast Enhancement of MRI Images using AHE and CLAHE Techniques

2019 ◽  
Vol 9 (2) ◽  
pp. 2442-2445 ◽  
Keyword(s):  
Image Processing ◽  
Contrast Enhancement ◽  
Image Enhancement ◽  
Medical Image ◽  
Histogram Equalization ◽  
Mri Scan ◽  
Adaptive Histogram Equalization ◽  
Shadowing Effect ◽  
Suitable Technique

Medical images require image enhancement, a category of image processing which provides better visualization that make diagnostic more accurate. The most commonly used method for improving the quality of medical image is Contrast enhancement.The main objective is to eliminate the use of contrast dye during the process of MRI scan and to find the parameters MSE, PSNR, AMBE and contrast and compare the result. The histogram equalization (HE) is the widely accepted method which is not productive when the contrast nature differs across the image. Adaptive Histogram Equalization (AHE) overcomes this limitation by considering and developing the mapping for each pixel from the histogram in a neighboring window. Another suitable technique is CLAHE. CLAHE is a refinement of AHE where the enhancement calculation is modified by imposing a user specified level to the height of local histogram. The enhancement is thereby reduced in very uniform areas of the image, which prevents over enhancement of noise and reduces the edge shadowing effect of unlimited AHE. After enhancing the image using AHE and CLAHE the comparison of their parameters is performed.

Analysis of color Image Enhancement Using DWT, Wavelet Shrinkageand FHE Methods

2017 ◽  
Vol 7 (8) ◽  
pp. 56
Author(s):  
Ashish Dwivedi ◽  
Nirupma Tiwari
Keyword(s):  
Image Enhancement ◽  
Color Image ◽  
Human Perception ◽  
Histogram Equalization ◽  
Wavelet Shrinkage ◽  
Visual Appearance ◽  
Enhancement Method ◽  
Fuzzy Histogram ◽  
Rgb Image

Image enhancement (IE) is very important in the field where visual appearance of an image is the main. Image enhancement is the process of improving the image in such a way that the resulting or output image is more suitable than the original image for specific task. With the help of image enhancement process the quality of image can be improved to get good quality images so that they can be clear for human perception or for the further analysis done by machines.Image enhancement method enhances the quality, visual appearance, improves clarity of images, removes blurring and noise, increases contrast and reveals details. The aim of this paper is to study and determine limitations of the existing IE techniques. This paper will provide an overview of different IE techniques commonly used. We Applied DWT on original RGB image then we applied FHE (Fuzzy Histogram Equalization) after DWT we have done the wavelet shrinkage on Three bands (LH, HL, HH). After that we fuse the shrinkage image and FHE image together and we get the enhance image.

scholarly journals Lung Cancer Detection using Convolutional Neural Network

2019 ◽  
Vol 8 (6) ◽  
pp. 3256-3262 ◽  
Keyword(s):  
Lung Cancer ◽  
Image Processing ◽  
Detection System ◽  
Histogram Equalization ◽  
Input Image ◽  
Machine Learning Algorithms ◽  
Morphological Operations ◽  
Image Processing Techniques ◽  
Cad System ◽  
Processing Techniques

The mortality rate is increasing among the growing population and one of the leading causes is lung cancer. Early diagnosis is required to decrease the number of deaths and increase the survival rate of lung cancer patients. With the advancements in the medical field and its technologies CAD system has played a significant role to detect the early symptoms in the patients which cannot be carried out manually without any error in it. CAD is detection system which has combined the machine learning algorithms with image processing using computer vision. In this research a novel approach to CAD system is presented to detect lung cancer using image processing techniques and classifying the detected nodules by CNN approach. The proposed method has taken CT scan image as input image and different image processing techniques such as histogram equalization, segmentation, morphological operations and feature extraction have been performed on it. A CNN based classifier is trained to classify the nodules as cancerous or non-cancerous. The performance of the system is evaluated in the terms of sensitivity, specificity and accuracy

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

scholarly journals Segmentasi Citra Menggunakan Metode Watershed Transform Berdasarkan Image Enhancement Dalam Mendeteksi Embrio Telur

2020 ◽  
Vol 5 (2) ◽  
pp. 53-60 ◽  
Author(s):  
Shoffan Saifullah
Keyword(s):  
Image Processing ◽  
Image Enhancement ◽  
Histogram Equalization ◽  
Watershed Transform ◽  
Adaptive Histogram Equalization

Image processing dapat diterapkan dalam proses deteksi embrio telur. Proses deteksi embrio telur dilakukan dengan menggunakan proses segmentasi, yang membagi citra sesuai dengan daerah yang dibagi. Proses ini memerlukan perbaikan citra yang diproses untuk memperoleh hasil optimal. Penelitian ini akan menganalisis deteksi embrio telur berdasarkan image processing dengan image enhancement dan konsep segmentasi menggunakan metode watershed transform. Image enhacement pada preprocessing dalam perbaikan citra menggunakan kombinasi metode Contrast Limited Adaptive Histogram Equalization (CLAHE) dan Histogram Equalization (HE). Citra grayscale telur diperbaiki dengan menggunakan metode CLAHE, dan hasilnya diproses kembali dengan menggunakan HE. Hasil perbaikan citra menunjukkan bahwa metode kombinasi CLAHE-HE memberikan gambar secara jelas daerah objek citra telur yang memiliki embrio. Proses segmentasi dengan menggunakan konversi citra ke citra hitam putih dan segmentasi watershed mampu menunjukkan secara jelas objek telur ayam yang memiliki embrio. Hasil segmentasi mampu membagi daerah telur memiliki embrio secara nyata dan akurat dengan persentase sebesar  » 98%.

An Image De-Noising Method Based on Intensity Histogram Equalization Technique for Image Enhancement

2018 ◽  
pp. 311-323
Author(s):  
Shantharajah S. P. ◽  
Ramkumar T ◽  
Balakrishnan N
Keyword(s):  
Image Enhancement ◽  
Mapping Function ◽  
Histogram Equalization ◽  
Noise Removal ◽  
False Positive Error ◽  
Intensity Histogram ◽  
Efficient Processing ◽  
Equalization Technique ◽  
Interactive Procedures ◽  
Different Levels

Image enhancement is a quantifying criterion for sharpening and enhancing image quality, where many techniques are empirical with interactive procedures to obtain précised results. The proposed Intensity Histogram Equalization (IHE) approach conquers the noise defects that has a preprocessor to remove noise and enhances image contrast, providing ways to improve the intensity of the image. The preprocessor has the mask production, enlightenment equalization and color normalization for efficient processing of the images which generates a binary image by labeling pixels, overcomes the non-uniform illumination of image and classifies color capacity, respectively. The distinct and discrete mapping function calculates the histogram values and improves the contrast of the image. The performance of IHE is based on noise removal ratio, reliability rate, false positive error measure, Max-Flow Computational Complexity Measure with NDRA and Variation HOD. As the outcome, the different levels of contrast have been significantly improved when evaluated against with the existing systems.

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