A novel predictor coefficient interpolation algorithm for enhancement of spatial resolution of images

doi:10.1109/APCCAS.2010.5774999

Proceedings Article•DOI•

A novel predictor coefficient interpolation algorithm for enhancement of spatial resolution of images

Vinit Jakhetiya¹, Sunil Prasad Jaiswal¹, Anil Kumar Tiwari²•Institutions (2)

LNM Institute of Information Technology¹, Indian Institutes of Technology²

01 Dec 2010-pp 140-143

TL;DR: It is shown that use of interpolated prediction coefficient causes insignificant loss in subjective as well as objective (PSNR) quality of the higher resolution (HR) image as compared with the PSNR obtained by the actual prediction coefficient and there is around 40% to 50% reduction in computational complexity.

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Abstract: This paper presents a novel algorithm for enhancement of spatial resolution of images. The proposed algorithm estimates a Least square based predictor of lower order and interpolates the coefficients of higher order predictor. We have reduced the predictor order form p to (p−1) that results into a saving of computational power. The proposed algorithm is generic that can be used with most of the LS based interpolation algorithms reported in literature. We have shown that use of interpolated prediction coefficient causes insignificant loss in subjective as well as objective (PSNR) quality of the higher resolution (HR) image as compared with the PSNR obtained by the actual prediction coefficient and there is around 40% to 50% reduction in computational complexity.

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Journal Article•DOI•

New edge-directed interpolation

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Xin Li, Michael T. Orchard

01 Oct 2001-IEEE Transactions on Image Processing

TL;DR: Simulation results demonstrate that the new interpolation algorithm substantially improves the subjective quality of the interpolated images over conventional linear interpolation.

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Abstract: This paper proposes an edge-directed interpolation algorithm for natural images. The basic idea is to first estimate local covariance coefficients from a low-resolution image and then use these covariance estimates to adapt the interpolation at a higher resolution based on the geometric duality between the low-resolution covariance and the high-resolution covariance. The edge-directed property of covariance-based adaptation attributes to its capability of tuning the interpolation coefficients to match an arbitrarily oriented step edge. A hybrid approach of switching between bilinear interpolation and covariance-based adaptive interpolation is proposed to reduce the overall computational complexity. Two important applications of the new interpolation algorithm are studied: resolution enhancement of grayscale images and reconstruction of color images from CCD samples. Simulation results demonstrate that our new interpolation algorithm substantially improves the subjective quality of the interpolated images over conventional linear interpolation.

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1,933 citations

"A novel predictor coefficient inter..." refers background or methods in this paper

...To preserve edge structures in interpolation, Li and Orchard proposed to estimate the covariance of high-resolution (HR) image from the covariance of the low-resolution (LR) image, and then interpolate the missing pixels based on the estimated covariance [ 1 ]....
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...Section 2 briefly discusses about the New edge directed interpolation method [ 1 ] and Single pass interpolation algorithm (SPIA) [6]....
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...Suppose an interpolator requires estimation of LS based predictor of order p. In this work, we estimate (p-1) - order LS based predictor and efficiently generate synthetic predictor of order p. To be specific, method NEDI [ 1 ] requires 4 order LS based predictor while we estimate 3 order predictor and using this predictor, we synthetically generate predictor of order 4. Similarly, Method SPIA [6] estimates both 4 order and 6 order LS based ......
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...A number of interpolation algorithms have been developed [ 1 ]-[6] which gives trade-off between computational simplicity and objective quality....
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...Our proposed image interpolation algorithm is compared with the existing Nearest Neighbor [5], Bilinear [5], Bicubic [5], NEDI [ 1 ], fused bi-directional interpolation [3] and SPIA [6]....
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Journal Article•DOI•

An edge-guided image interpolation algorithm via directional filtering and data fusion

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Lei Zhang, Xiaolin Wu¹•Institutions (1)

McMaster University¹

01 Aug 2006-IEEE Transactions on Image Processing

TL;DR: A new edge-guided nonlinear interpolation technique is proposed through directional filtering and data fusion that can preserve edge sharpness and reduce ringing artifacts in image interpolation algorithms.

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Abstract: Preserving edge structures is a challenge to image interpolation algorithms that reconstruct a high-resolution image from a low-resolution counterpart. We propose a new edge-guided nonlinear interpolation technique through directional filtering and data fusion. For a pixel to be interpolated, two observation sets are defined in two orthogonal directions, and each set produces an estimate of the pixel value. These directional estimates, modeled as different noisy measurements of the missing pixel are fused by the linear minimum mean square-error estimation (LMMSE) technique into a more robust estimate, using the statistics of the two observation sets. We also present a simplified version of the LMMSE-based interpolation algorithm to reduce computational cost without sacrificing much the interpolation performance. Experiments show that the new interpolation techniques can preserve edge sharpness and reduce ringing artifacts

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971 citations

"A novel predictor coefficient inter..." refers background or methods in this paper

...Alternatively, Zhang and Wu proposed to interpolate a missing pixel in multiple directions, and then fuse the directional interpolation results by minimum mean square-error estimation [3]....
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...Our proposed image interpolation algorithm is compared with the existing Nearest Neighbor [5], Bilinear [5], Bicubic [5], NEDI [1], fused bi-directional interpolation [3] and SPIA [6]....
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Journal Article•DOI•

Image Interpolation by Adaptive 2-D Autoregressive Modeling and Soft-Decision Estimation

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Xiangjun Zhang¹, Xiaolin Wu¹•Institutions (1)

McMaster University¹

01 Jun 2008-IEEE Transactions on Image Processing

TL;DR: A soft-decision interpolation technique that estimates missing pixels in groups rather than one at a time, which preserves spatial coherence of interpolated images better than the existing methods and produces the best results so far over a wide range of scenes in both PSNR measure and subjective visual quality.

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Abstract: The challenge of image interpolation is to preserve spatial details. We propose a soft-decision interpolation technique that estimates missing pixels in groups rather than one at a time. The new technique learns and adapts to varying scene structures using a 2-D piecewise autoregressive model. The model parameters are estimated in a moving window in the input low-resolution image. The pixel structure dictated by the learnt model is enforced by the soft-decision estimation process onto a block of pixels, including both observed and estimated. The result is equivalent to that of a high-order adaptive nonseparable 2-D interpolation filter. This new image interpolation approach preserves spatial coherence of interpolated images better than the existing methods, and it produces the best results so far over a wide range of scenes in both PSNR measure and subjective visual quality. Edges and textures are well preserved, and common interpolation artifacts (blurring, ringing, jaggies, zippering, etc.) are greatly reduced.

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588 citations

"A novel predictor coefficient inter..." refers methods in this paper

...D Autoregressive Modeling and Soft-Decision Estimation [4]....
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...Zhang and Wu proposed an Image Interpolation algorithm based on the Adaptive 2-D Autoregressive Modeling and Soft-Decision Estimation [4]....
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Journal Article•DOI•

Subpixel edge localization and the interpolation of still images

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K. Jensen, D. Anastassiou¹•Institutions (1)

Columbia University¹

01 Mar 1995-IEEE Transactions on Image Processing

TL;DR: The algorithm is based on a source model emphasizing the visual integrity of detected edges and incorporates a novel edge fitting operator that has been developed for this application, and produces an image of increased resolution with noticeably sharper edges and lower mean-squared reconstruction error than that produced by linear techniques.

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Abstract: In this paper, we present a nonlinear interpolation scheme for still image resolution enhancement. The algorithm is based on a source model emphasizing the visual integrity of detected edges and incorporates a novel edge fitting operator that has been developed for this application. A small neighborhood about each pixel in the low-resolution image is first mapped to a best-fit continuous space step edge. The bilevel approximation serves as a local template on which the higher resolution sampling grid can then be superimposed (where disputed values in regions of local window overlap are averaged to smooth errors). The result is an image of increased resolution with noticeably sharper edges and, in all tried cases, lower mean-squared reconstruction error than that produced by linear techniques. >

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492 citations

"A novel predictor coefficient inter..." refers background in this paper

...Jensen and Anastassiou published a scheme that detects edges and fits them with some templates to improve the visual perception of interpolated images [2]....
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Journal Article•DOI•

Computational foundations of image interpolation algorithms

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Tinku Acharya¹, Ping-Sing Tsai²•Institutions (2)

Arizona State University¹, University of Texas–Pan American²

01 Oct 2007-Ubiquity

TL;DR: The underlying computational foundations of all these algorithms and their implementation techniques are described and some experimental results are presented to show the impact of these algorithms in terms of image quality metrics and computational requirements for implementation.

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Abstract: Image interpolation is an important image processing operation applied in diverse areas ranging from computer graphics, rendering, editing, medical image reconstruction, to online image viewing. Image interpolation techniques are referred in literature by many terminologies, such as image resizing, image resampling, digital zooming, image magnification or enhancement, etc. Basically, an image interpolation algorithm is used to convert an image from one resolution (dimension) to another resolution without loosing the visual content in the picture. Image interpolation algorithms can be grouped in two categories, non-adaptive and adaptive. The computational logic of an adaptive image interpolation technique is mostly dependent upon the intrinsic image features and contents of the input image whereas computational logic of a non-adaptive image interpolation technique is fixed irrespective of the input image features. In this paper, we review the progress of both non-adaptive and adaptive image interpolation techniques. We also proposed a new algorithm for image interpolation in discrete wavelet transform domain and shown its efficacy. We describe the underlying computational foundations of all these algorithms and their implementation techniques. We present some experimental results to show the impact of these algorithms in terms of image quality metrics and computational requirements for implementation.

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73 citations

"A novel predictor coefficient inter..." refers background or methods in this paper

...The traditional interpolation algorithms [5] are computationally simple but not able to perform well near the rapidly varying structure....
[...]
...Our proposed image interpolation algorithm is compared with the existing Nearest Neighbor [5], Bilinear [5], Bicubic [5], NEDI [1], fused bi-directional interpolation [3] and SPIA [6]....
[...]

A novel predictor coefficient interpolation algorithm for enhancement of spatial resolution of images

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"A novel predictor coefficient inter..." refers background or methods in this paper

"A novel predictor coefficient inter..." refers background or methods in this paper

"A novel predictor coefficient inter..." refers methods in this paper

"A novel predictor coefficient inter..." refers background in this paper

"A novel predictor coefficient inter..." refers background or methods in this paper

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