Fast Convergence Strategy for Multi-Image Superresolution via Adaptive Line Search

Adaptive search area for fast motion estimation

In this paper a new method for determining the search area for motion estimation algorithm based on block matching is suggested. In the proposed method the search area is adaptively found for each block of a frame. This search area is similar to that of the full search (FS) algorithm but smaller for most blocks of a frame. Therefore, the proposed algorithm is analogous to FS in terms of reg...

Fast Superresolution for Image Sequences Using Motion Adaptive Relaxation Parameters

SAR Image Superresolution via 2-D Adaptive Extrapolation

In this paper, we present a description of a nonparametric two dimensional (2-D) procedure to extrapolate a signal, an extension of the Adaptive Weighted Norm Extrapolation (AWNE) method, and illustrate its application to SAR image formation. The benefits of the AWNE procedure are shown for synthetic data and for MSTAR data. Once the phase history is recovered, the AWNE method is applied to a s...

Superresolution Image Reconstruction Using Fast Inpainting Algorithms

The main aim of this paper is to employ the total variation (TV) inpainting model to superresolution imaging problems. We focus on the problem of reconstructing a highresolution image from several decimated, blurred and noisy low-resolution versions of the high-resolution image. We propose a general framework for multiple shifted and multiple blurred low-resolution image frames which subsumes s...

Image Superresolution Reconstruction via Granular Computing Clustering

The problem of generating a superresolution (SR) image from a single low-resolution (LR) input image is addressed via granular computing clustering in the paper. Firstly, and the training images are regarded as SR image and partitioned into some SR patches, which are resized into LS patches, the training set is composed of the SR patches and the corresponding LR patches. Secondly, the granular ...