Similarity modulated block estimation for image interpolation

2015 International Conference on Informatics, Electronics & Vision (ICIEV), 2015

In this paper, we propose a new image interpolation method using adaptive weights based on inverse gradients and distances from the pixels used in prediction. Since the weights are based on different spatial locations of the pixels, this allows preservation of important edge information and hence to prevent extensive blurring across edges in the upsampling process. Experimental results on a large test image set show that the proposed algorithm gives better performance compared to conventional algorithms.

2015

In current digital era the image interpolation techniques based on multi-resolution technique are being discovered and developed. These techniques are gaining importance due to their application in variety if field (medical, geographical, space information) where fine and minor details are important. This paper presents an overview of different interpolation techniques, (nearest neighbor, Bilinear, Bicubic, B-spline, Lanczos, Discrete wavelet transform (DWT) and Kriging). Our results show bicubic interpolations gives better results than nearest neighbor and bilinear, whereas DWT and Kriging give finer details.

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.

Intelligent Signal Processing …, 2010

This paper proposes a new approach for interpolating natural images. Unlike other conventional interpolation methods, we exploit the characteristics of an image and decompose it into a texture part and a non-texture (cartoon) part. In the non-texture part, we interpolate it with bicubic interpolation which performs well for smooth areas. We deal the texture part with peak transform based interpolation which resets the image pixels to capture the characters of the texture through peak transform. The experiment results demonstrate our new interpolation method has higher PSNR and better subjective quality of the interpolated image, especially in the texture part.

Acoustics, Speech, and Signal …, 2001

We consider the problem of image interpolation using adaptive optimal recovery. We adaptively estimate the local quadratic signal class of our image pixels. We then use optimal recovery to estimate the missing local samples based on this quadratic signal class. This approach tends preserve edges, interpolating along edges and not across them.

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

International Journal of Computer Applications, 2012

Interpolation is the process of transferring image from one resolution to another without losing image quality. In Image processing field, image interpolation is very important function for doing zooming, enhancement of image, resizing any many more. Here in this paper, we have reviewed different interpolation algorithms. We have implemented all reviewed interpolation algorithms and done comparison of all.

2006 International Conference on Image Processing, 2006

In this paper we present a novel method for interpolating images and we introduce the concept of non-local interpolation. Unlike other conventional interpolation methods, the estimation of the unknown pixel values is not only based on its local surrounding neighbourhood, but on the whole image (nonlocally). In particularly, we exploit the repetitive character of the image. A great advantage of our proposed approach is that we have more information at our disposal, which leads to better estimates of the unknown pixel values. Results show the effectiveness of non-local interpolation and its superiority at very large magnifications to other interpolation methods.

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.

Image interpolation based on the pixel value corresponding to the smallest absolute difference, 2011

This paper proposes a novel algorithm for image interpolation. The motivation is based on relentless image interpolation artefacts and computational complexity. The solution proposed is founded on reprocessing one of the four pixels surrounding the unknown location and calculating the mean between that pixel and the value introduced by the bilinear interpolation. Subsequently, the mean is multiplied by the control factor k whose value is selected according to experimental analysis. Experimental results are further presented to show the effectiveness and performance of the proposed algorithm.