Ashok Kumar

Bio: Ashok Kumar is an academic researcher from LNM Institute of Information Technology. The author has contributed to research in topics: Image scaling & Image processing. The author has an hindex of 2, co-authored 2 publications receiving 19 citations.

Image interpolation by adaptive 2-D autoregressive modeling

Abstract: This paper presents a new interpolation algorithm based on the adaptive 2-D autoregressive modeling. The algorithm uses a piece-wise autoregressive (PAR) model to predict the unknown pixels of high resolution image. For this purpose, we used a block-based prediction model to predict the unknown pixels. The unknown pixels are categorized into three categories and they are predicted using predictors of different structure and order. Prediction accuracy and the visual quality of the interpolated image depend on the size of the window. We experimentally found an appropriate window size and have shown that subjective as well as objective (PSNR) quality of the high resolution (HR) images is same, on an average, as that of the competitive such method reported in literature and also the method is a single pass.

A survey on image interpolation methods

Abstract: In this paper we are describing some important state-of the-art algorithms used for Image interpolation.These algorithms are broadly classified as prediction based and transform based methods. Motivation behind this work is to provide new researchers a detailed analysis of such algorithms in the context of artifacts, subjective and objective quality of interpolated image, computational cost and to give future research direction based on the analysis. However, the goal of this study was not to determine an overall best method, but to present a comprehensive catalogue of methods in a uniform terminology, to define general properties and requirements local techniques, and to enable the reader to select that method which is optimal for his specific application.

An efficient image interpolation algorithm based upon the switching and self learned characteristics for natural images

Abstract: This paper presents a new image interpolation technique for enhancement of spatial resolution of images. The proposed algorithm uses the switching of existing Soft-decision Adaptive Interpolation (SAI) algorithm and Single Pass Interpolation Algorithm (SPIA) methods. We learn the error pattern in the interpolation process of SAI method and SPIA Method after interpolating downsampled version of LR image. Then we deviced a mechanism to correct the error pattern. Emperically we found that SAI methods works better on smooth images (variation among the pixels is less) while SPIA method works better on detailed images (more variation among the pixels), because of the type of pixels used in the interpolation. So, a hybrid scheme of combining SAI method and SPIA method is proposed for best prediction of high resolution (HR) image. The proposed algorithm produces the best results in different varieties of images in terms of both PSNR measurement and subjective visual quality.

Edge-based video interpolation for video and image upsampling

Abstract: Edge-based interpolation for upsampling. One method may include determining an edge characteristic associated with an interpolation point, the edge characteristic having an edge magnitude and an edge angle; selecting an interpolation filter in response to the edge angle; and determining a pixel value at the interpolation point using the selected interpolation filter. Other embodiments include edge-based interpolation followed by an adaptive sharpening filter. The sharpening filter is controlled by the edge-based interpolation parameters that determine the pixels to be sharpened and the sharpening strength.

Multi-frame image super-resolution reconstruction via low-rank fusion combined with sparse coding

Abstract: The sparse coding method has been successfully applied to multi-frame super-resolution in recent years. In this paper, we propose a new multi-frame super-resolution framework which combines low-rank fusion with sparse coding to improve the performance of multi-frame super-resolution. The proposed method gets the high-resolution image by a three-stage process. First, a fused low-resolution image is obtained from multi-frame image by the method of registration and low-rank fusion. Then, we use the jointly training method to train a pair of learning dictionaries which have good adaptive ability. Finally, we use the learning dictionaries combined with sparse coding theory to realize super-resolution reconstruction of the fused low-resolution image. As the experiment results show, this method can recover the lost high frequency information, and has good robustness.

A fast edge-directed interpolation algorithm

Abstract: Image interpolation is a method of obtaining a high resolution image from a low resolution image, which is applied to many image processing procedures In order to make the interpolated image having smooth edges and make the interpolation processing fast, we propose a fast edge-directed interpolation algorithm in this paper The proposed method consists of three steps, the determination of nonedge pixels and edge pixels, the bilinear interpolation for nonedge pixels, and the edge-adaptive interpolation for edge pixels The experimental results show that it outperforms some existing interpolation algorithms in terms of image quality and processing speed

A computationally efficient context based switched image interpolation algorithm for natural images

Abstract: In this paper we proposed a new computationally efficient interpolation algorithm for natural images in which unknown pixels are divided into few bins. The categorization of these unknown pixels into bins is based upon the characteristics of the neighboring pixels. These characteristics are obtained by taking difference of two slopes which are in orthogonal direction and these slopes are calculated from a set of neighboring pixels. We used the Least-Squares (LS) based approach to find optimal predictors for pixels belonging to various slope bins. We also presented a simplified proposed algorithm in which we used bilinear interpolation algorithm instead of estimating LS based predictor for some bins and it results into further reduction in computational complexity without sacrificing the much performance. Our proposed algorithm gives better interpolation quality with significantly lower computational complexity as compared to recently reported interpolation algorithms.