A New Orientation-Adaptive Interpolation Method

Journal of Electronic Imaging

We present a modification of the new edge-directed interpolation method that eliminates the prediction error accumulation problem by adopting a modified training window structure, and extending the covariance matching into multiple directions to suppress the covariance mismatch problem. Simulation results show that the proposed method achieves remarkable subjective performance in preserving the edge smoothness and sharpness among other methods in the literaturé. It also demonstrates consistent objective performance among a variety of images.

IEEE Access

This paper presents an image interpolation method that provides superior performance with low complexity. Applying a simple linear method achieves time-efficient interpolation, but often produces artifacts; however, applying other complex methods reduce unwanted artifacts at the cost of high computation time. The proposed interpolation scheme is based on the improvement of an algorithm called "Edge Slope Tracing (EST)" that predicts the slope based on the information of the adjacent slopes. Predicted slopes are used in slope-based line averaging to perform the interpolation. Slope-based line averaging is followed by post-processing of an interpolated image using two-way interpolation and thin edge correction to avoid the production of unwanted artifacts at the cost of low complexity. The proposed algorithm is very simple and simulation results indicate that it provides better or nearly equal results compared to conventional state-of-the-art algorithms and that it also reduces complexity to a great extent compared to conventional methods.

IJARCCE, 2016

Image interpolation is the most basic requirement for many image processing task such as computer graphics, gaming, medical image processing, virtualization, camera surveillance and quality control. Image interpolation is a technique used in resizing images. To resize an image, every pixel in the new image must be mapped back to a location in the old image in order to calculate a value of new pixel. There are many algorithms available for determining new value of the pixel, most of which involve some form of interpolation among the nearest pixels in the old image. In this paper, we used Nearest-neighbor, Bilinear, Bicubic, Bicubic B-spline, Catmull-Rom, Mitchell-Netravali and Lanzcos of order three algorithms for image interpolation. Each algorithms generates varies artifact such as aliasing, blurring and moiré. This paper presents, quality and computational time consideration of images while using these interpolation algorithms.

A new pixel-by-pixel (PBP) cubic image interpolation algorithm that preserves image edges is suggested in this paper. The PBP approach is based on optimizing the standard cubic image interpolation formula at each estimated pixel. Thus, the mean square error (MSE) in the entire image is minimized. A study of the effect of optimizing the cubic image interpolation formula with respect to the separated or combined parameters of the formula is presented. The optimum values of the parameters are estimated iteratively at each pixel. The performance of the suggested approach is tested in the presence of different noise levels and is compared with the traditionaI warped distance adaptive image interpolation technique. The obtained results proves the superiority of the suggested PBP cubic image interpolation algorithm as compared to the t~a d i t i 0~1 algorithm from both of the MSE and edge preservation points of view.

2014 International Conference on Informatics, Electronics & Vision (ICIEV), 2014

Quality degradation and computational complexity are the major challenges for image interpolation algorithms. Advanced interpolation techniques achieve to preserve fine image details but typically suffer from lower computational efficiency, while simpler interpolation techniques lead to lower quality images. In this paper, we propose an edge preserving technique based on inverse gradient weights as well as pixel locations for interpolation. Experimental results confirm that the proposed algorithm exhibits better image quality compared to conventional algorithms. At the same time, our approach is shown to be faster than several advanced edge preserving interpolation algorithms.

Brazilian Symposium on Computer Graphics and Image Processing, 2002

Digital image interpolation techniques are frequently used to enlarge pictures, i.e. zooming in, and they are upon increasingly demand for developing product applications using digital still cameras. One common difficulty with conventional interpolation techniques is that of preserving details, i.e. edges, and at the same time smoothing the data for not introducing spurious artifacts. A definitive solution to this is

2010 Proceedings of 19th International Conference on Computer Communications and Networks, 2010

In this paper, we present an adaptive directional window selection for the edge-directed interpolation. The new window selection can solve the problem of covariance mismatch in high frequency and texture regions. It makes use of a practical directional elliptic window which works according to the edge direction sliding along an edge and then subsequently chooses the best window evaluated by choosing the elliptic window which has the lowest Means Square Error (MSE). Experimental results show that by the proposed technique can generate a high quality interpolated image which is better than other edge directed interpolation approaches. Experimental results also provided on different images to justify the value of this approach at the end of the paper.

2008

In this paper we analyse the problem of general purpose image upscaling that preserves edge features and natural appearance and we present the results of subjective and objective evaluation of images interpolated using different algorithms. In particular, we consider the well-known NEDI (New Edge Directed Interpolation, Li and Orchard, 2001) method, showing that by modifying it in order to reduce numerical instability and making the region used to estimate the low resolution covariance adaptive, it is possible to obtain relevant improvements in the interpolation quality. The implementation of the new algorithm (iNEDI, improved New Edge Directed Interpolation), even if computationally heavy (as the Li and Orchard's method), obtained, in both subjective and objective tests, quality scores that are notably higher than those obtained with NEDI and other methods presented in the literature.

In the contribution we develop a new method for object-orientedinterpolation of images. It is an important tool in image processing,since using interpolation we can considerably decrease amount of data,necessary for image reconstruction. Application of this interpolationenables to down-sample the object separately. The selected object canbe processed at the different sampling level. This approach allowsobject oriented zoom, for example. Moreover, object - oriented approachis a very novel idea that helps to understand the content of image.Method is created from cosine approximation implemented to coding withshape - independent basis functions.

2011 IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC), 2011

A novel algorithm called "edge-preserving pixel-level interpolation by triangulation" is proposed for interpolating the image. This algorithm can be used in arbitrary resolution enhancement and solve the problem of edge preserving in interpolating the input image. It is simpler than other conventional methods and is adaptable to a various image process. The basic idea is to first estimate the direction of edge in a small region and then use different way to deal with each situation. The most essential point is to maintain the Parallelism of lines around edges. Two concepts have been considered in interpolating process: (1) edge direction confirm (2) parallel line preserved. Good experiments results showing the effectiveness of edge preserving and its superiority to other methods are also included.