Ashish Khare

Work place: J.K. Institute of Applied Physics and Technology, University of Allahabad, Allahabad (U.P.) 211002, INDIA



Research Interests: Models of Computation, Computer Architecture and Organization, Computer Vision, Computer systems and computational processes


Ashish Khare, is an assistant professor in Computer Science at the University of Allahabad, Allahabad (U.P.) INDIA. He has completed D.Phil. (Computer Science) from University of Allahabad, Allahabad (U.P.) in 2007. He has published several papers in refereed international journals and conference proceedings. He has been associated as a Post Doctoral Fellow at Gwangju Institute of Science and Technology, Gwangju, Korea during 2007-2008. His research areas include image processing and computer vision, soft computing, applications of wavelet transform.

Author Articles
Threshold based Image Fusion in Dual Tree Complex Wavelet Domain

By Richa Srivastava Ashish Khare

DOI:, Pub. Date: 8 Oct. 2016

Image fusion is a popular application of image processing which performs merging of two or more images into one. The merged image is of improved visual quality and carries more information content. The present work introduces a new image fusion method in complex wavelet domain. The proposed fusion rule is based on a level dependent threshold, where absolute difference of a wavelet coefficient from the threshold value is taken as fusion criteria. This absolute difference represents variation in the image intensity that resembles the salient features of image. Hence, for fusion, the coefficients that are far from threshold value are being selected. The motivation behind using dual tree complex wavelet transform is due to failure of real valued wavelet transform in many aspects. Good directional selectivity, availability of phase information and approximate shift invariant nature of dual tree complex wavelet transform make it suitable for image fusion and help to produce a high quality fused image. To prove the strength of the proposed method, it has been compared with several spatial, pyramidal, wavelet and new generation wavelet based fusion methods. The experimental results show that the proposed method outperforms all the other state-of-the-art methods visually as well as in terms of standard deviation, mutual information, edge strength, fusion factor, sharpness and average gradient. 

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Classification of Alzheimer Disease using Gabor Texture Feature of Hippocampus Region

By Prateek Keserwani V. S. Chandrasekhar Pammi Om Prakash Ashish Khare Moongu Jeon

DOI:, Pub. Date: 8 Jun. 2016

The aim of this research is to propose a methodology to classify the subjects into Alzheimer disease and normal control on the basis of visual features from hippocampus region. All three dimensional MRI images were spatially normalized to the MNI/ICBM atlas space. Then, hippocampus region was extracted from brain structural MRI images, followed by application of two dimensional Gabor filter in three scales and eight orientations for texture computation. Texture features were represented on slice by slice basis by mean and standard deviation of magnitude of Gabor response. Classification between Alzheimer disease and normal control was performed with linear support vector machine. This study analyzes the performance of Gabor texture feature along each projection (axial, coronal and sagittal) separately as well as combination of all projections. The experimental results from both single projection (axial) as well as combination of all projections (axial, coronal and sagittal), demonstrated better classification performance over other existing method. Hence, this methodology could be used as diagnostic measure for the detection of Alzheimer disease. 

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Restoration of Degraded Gray Images Using Genetic Algorithm

By Dhirendra Pal Singh Ashish Khare

DOI:, Pub. Date: 8 Mar. 2016

This Image deblurring aims to eliminate or decrease the degradations that has been occurred while the image has been obtained. In this paper, we proposed a unified framework for restoration process by enhancement and more quantified deblurred images with the help of Genetic Algorithm. The developed method uses an iterative procedure using evolutionary criteria and produce better images with most restored frequency-content. We have compared the proposed methods with Lucy-Richardson Restoration method, method proposed by W. Dong [34] and Inverse Filter Restoration Method; and demonstrated that the proposed method is more accurate by achieving high quality visualized restored images in terms of various statistical quality measures.

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Text Region Extraction: A Morphological Based Image Analysis Using Genetic Algorithm

By Dhirendra Pal Singh Ashish Khare

DOI:, Pub. Date: 8 Jan. 2015

Image analysis belongs to the area of computer vision and pattern recognition. These areas are also a part of digital image processing, where researchers have a great attention in the area of content retrieval information from various types of images having complex background, low contrast background or multi-spectral background etc. These contents may be found in any form like texture data, shape, and objects. Text Region Extraction as a content from an mage is a class of problems in Digital Image Processing Applications that aims to provides necessary information which are widely used in many fields medical imaging, pattern recognition, Robotics, Artificial intelligent Transport systems etc. To extract the text data information has becomes a challenging task. Since, Text extraction are very useful for identifying and analysis the whole information about image, Therefore, In this paper, we propose a unified framework by combining morphological operations and Genetic Algorithms for extracting and analyzing the text data region which may be embedded in an image by means of variety of texts: font, size, skew angle, distortion by slant and tilt, shape of the object which texts are on, etc. We have established our proposed methods on gray level image sets and make qualitative and quantitative comparisons with other existing methods and concluded that proposed method is better than others.

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Evolutionary Image Enhancement Using Multi-Objective Genetic Algorithm

By Dhirendra Pal Singh Ashish Khare

DOI:, Pub. Date: 8 Nov. 2013

Image Processing is the art of examining, identifying and judging the significances of the Images. Image enhancement refers to attenuation, or sharpening, of image features such as edgels, boundaries, or contrast to make the processed image more useful for analysis. Image enhancement procedures utilize the computers to provide good and improved images for study by the human interpreters. In this paper we proposed a novel method that uses the Genetic Algorithm with Multi-objective criteria to find more enhance version of images. The proposed method has been verified with benchmark images in Image Enhancement. The simple Genetic Algorithm may not explore much enough to find out more enhanced image. In the proposed method three objectives are taken in to consideration. They are intensity, entropy and number of edgels. Proposed algorithm achieved automatic image enhancement criteria by incorporating the objectives (intensity, entropy, edges). We review some of the existing Image Enhancement technique. We also compared the results of our algorithms with another Genetic Algorithm based techniques. We expect that further improvements can be achieved by incorporating linear relationship between some other techniques.

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