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

2017 ◽  
pp. 121-132
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.

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.

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 New Combined Technique for Fingerprint Image Enhancement

2016 ◽  
Vol 11 (1) ◽  
pp. 222 ◽  
Author(s):  
Alaa Ahmed Abbood ◽  
Mohammed Sabbih Hamoud Al-Tamimi ◽  
Sabine U. Peters ◽  
Ghazali Sulong
Keyword(s):  
Image Enhancement ◽  
Histogram Equalization ◽  
Fingerprint Image ◽  
Pixel Intensity ◽  
Improve Image Quality ◽  
Enhancement Method ◽  
Equalization Technique ◽  
Fingerprint Image Enhancement ◽  
Normalization Process ◽  
Combined Technique

This paper presents a combination of enhancement techniques for fingerprint images affected by different type of noise. These techniques were applied to improve image quality and come up with an acceptable image contrast. The proposed method included five different enhancement techniques: Normalization, Histogram Equalization, Binarization, Skeletonization and Fusion. The Normalization process standardized the pixel intensity which facilitated the processing of subsequent image enhancement stages. Subsequently, the Histogram Equalization technique increased the contrast of the images. Furthermore, the Binarization and Skeletonization techniques were implemented to differentiate between the ridge and valley structures and to obtain one pixel-wide lines. Finally, the Fusion technique was used to merge the results of the Histogram Equalization process with the Skeletonization process to obtain the new high contrast images. The proposed method was tested in different quality images from National Institute of Standard and Technology (NIST) special database 14. The experimental results are very encouraging and the current enhancement method appeared to be effective by improving different quality images.

scholarly journals A Novel Technique for Automatic Image Enhancement using HTHET Approach

2019 ◽  
Vol 8 (1) ◽  
pp. 26-31
Author(s):  
V. Murali ◽  
T. Venkateswarlu
Keyword(s):  
Contrast Enhancement ◽  
Image Enhancement ◽  
Information Content ◽  
Optimization Method ◽  
Histogram Equalization ◽  
Original Image ◽  
Enhancement Technique ◽  
Novel Technique ◽  
Equalization Technique ◽  
Histogram Transformation

Image enhancement techniques are methods used for producing images with better quality than the original image. None of the existing methods increase the information content of the image, and are usually of little interest for subsequent automatic analysis of images. In this paper, automated Image Enhancement is achieved by carrying out Histogram techniques. Histogram equalization (HE) is a spatial domain image enhancement technique, which effectively enhances the contrast of an image. We make use of Transformation and Hyperbolization techniques for automatic image enhancement. However, while it takes care of contrast enhancement, a modified histogram equalization technique, Histogram Transformation and Hyperbolization Equalization Technique (HTHET) using optimization method is proposed using EQHIST and LINHIST.

scholarly journals Gradient, Texture Driven Based Dynamic-Histogram Equalization For Medical Image Enhancement

2021 ◽  
Vol 15 ◽  
pp. 303-310
Author(s):  
H. N. Vidyasaraswathi ◽  
M. C. Hanumantharaju
Keyword(s):  
Image Enhancement ◽  
Medical Image ◽  
Medical Images ◽  
Similarity Index ◽  
Mapping Function ◽  
Structural Similarity ◽  
Histogram Equalization ◽  
Visual Appearance ◽  
Index Measurement ◽  
Medical Image Enhancement

In many clinical diagnostic measurements, medical images play some significant role but often suffer from various types of noise and low-luminance, which causes some notable changes in overall system accuracy with misdiagnosis rate. To improve the visual appearance of object regions in medical images, image enhancement techniques are used as potential pre-processing techniques. Due to its simplicity and easiness of implementation, histogram equalization is widely preferred in many applications. But due to its mapping function based image transformation during enhancement process affect the biomedical patterns which are essential for diagnosis. To mitigate these issues in medical images, a new method based on gradient computations and Texture Driven based Dynamic histogram equalization (GTDDHE) is accomplished to increase the visual perception. The spatial texture pattern is also included to ensure the texture retention and associated control over its variations during histogram modifications. Experimental results on MRI, CT images, eyes images from medical image datasets and quantitative analysis by PSNR, structural similarity index measurement (SSIM), information entropy (IE) and validated that the proposed method offers improved quality with maximum retention of biomedical patterns across all types of medical images.

INVESTIGATION OF HISTOGRAM EQUALIZATION FILTER FOR CT SCAN IMAGE ENHANCEMENT

2019 ◽  
Vol 31 (05) ◽  
pp. 1950038
Author(s):  
Ayesha Amir Siddiqi ◽  
Ghous Bakhsh Narejo ◽  
Mashal Tariq ◽  
Adnan Hashmi
Keyword(s):  
Image Enhancement ◽  
Quantum Noise ◽  
Signal To Noise Ratio ◽  
Histogram Equalization ◽  
Noise Removal ◽  
Ct Images ◽  
Liver Tumour ◽  
Clinical Images ◽  
Computed Tomographic ◽  
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.

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

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