Abstract
Abstract This paper presents a non-iterative regularized inverse solution to the image interpolation problem. This solution is based on the segmentation of the image to be interpolated into overlapping blocks and the interpolation of each block, separately. The purpose of the overlapping blocks is to avoid edge effects. A global regularization parameter is used in interpolating each block. In this solution, a single matrix inversion process of moderate dimensions is required in the whole interpolation process. Thus, it avoids the large computational cost due to the matrices of large dimensions involved in the interpolation process. The performance of this approach is compared to the standard iterative regularized interpolation scheme and to polynomial based interpolation schemes such as the bicubic and cubic spline techniques. A comparison of the suggested approach with some algorithms implemented in the commercial ACDSee software has been performend in the paper. The obtained results reveal that the suggested solution has a better performance as compared to other algorithms from the MSE and the edges preservation points of view. Its computation time is relatively large as compared to traditional algorithms but this is acceptable when image quality is the main concern.
Highlights
Image interpolation is the process by which a high resolution (HR) image is obtained from a low resolution (LR) one
We suggest a new implementation of the regularized image interpolation algorithm
This paper suggests an efficient implementation of the regularized image interpolation problem as an inverse problem
Summary
Image interpolation is the process by which a high resolution (HR) image is obtained from a low resolution (LR) one. These conventional algorithms are space invariant algorithms based on the appropriate choice of a basis function They don’t consider the spatial activities of the image to be interpolated. Adaptive variants of the above mentioned algorithms have been developed [13,14,15] These adaptive algorithms improve the quality of the interpolated image especially near edges, they still don’t consider the mathematical model by which the image capturing devices operate. We suggest a new implementation of the regularized image interpolation algorithm In this suggested implementation, we solve the problem using a non-iterative inverse solution. This implementation requires a single matrix inversion of moderate dimensions if a global regularization parameter is used
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