Earticle

This paper introduces a novel solution to generate a super-resolution image from a set of low-resolution input based on patch information. Recent research has shown that super-resolved data can be reconstructed from an extremely small set of measurements compared to that currently required. This paper incorporates the compressive sensing framework to the reconstruction model. Moreover, in order to remove outliers introduced by image parallax, the supervised patch-adaptive matching method which uses photometrical similarity and geometrical distance to determine the matching patch is proposed to reconstruct the high resolution image. The performance of the proposed algorithm on both synthetic and real images is evaluated with several grayscale and color image sequences and found successful when compared to other algorithms.

All the time, there is a demand of High-Resolution (HR) images in electronic imaging applications. Super-Resolution (SR) is an approach used to restore High-Resolution (HR) image from one or more Low-Resolution (LR) images. The goal of SR is to extract the independent information from each LR image in that set and combine the information into a single high resolution (HR) image. The quality of reconstructed SR image obtained from a set of LR images depends upon the registration accuracy of LR images. In this paper SR reconstruction using a sub-pixel shift image registration and Fast Discrete Curvelet transform (FDCT) for image interpolation is proposed. The Curvelet transform is a multiscale pyramid with many directions and positions at each scale. Image interpolation is performed at the finest level in Curvelet domain. Experimentation based results have shown appropriate improvements in PSNR and MSE. Also, it is experimentally verified that the computational complexity of the SR algorithm is reduced.

Conventional depth extraction in integral imaging is based on the disparity information between the elemental images. Since the disparity is measured in pixel unit, however, the extracted depth is discrete, resulting in the quantization error. Moreover, the quantization error grows as the object depth increases, which limits the accuracy of the depth extraction for distant objects. In this paper, we propose a new method for depth extraction in integral imaging using sub-pixel registration information between subimages to obtain linear and accurate depth.