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This paper presents a learning-based method called image super-resolution (SR) for generating a high-resolution (HR) image from a single low-resolution (LR) image. Recent research investigated the image SR problem using sparse coding, which is based on good reconstruction of any image local patch by a sparse linear combination of atoms from an overcomplete dictionary. However, sparse-coding-based...
Recent years have seen an increasing interest in codebook-based model(bag-of-words-BOW) for image representation, which includes the basic bag-of-words model and its improved version for local descriptor reconstruction with sparse coding (SC) and locality-constrained linear coding (LLC) etc. Although the recent coding strategies in the BoW model can lead to prospect performance using large amounts...
Compressive sensing (CS) has recently attracted much attention due to its unique feature of directly and simultaneously acquiring compressed and encrypted data based on their sparse or compressible properties. To securely transmit compressively sensed multimedia data over networks, it is required to support transcoder to securely convert compressed multimedia into several different types for diverse...
In this paper, we propose a penalized ℓ1 minimization algorithm for reconstructing a time-varying signal based on compressive sensing (CS) principles. The time-varying signal can be seen as a sequence of slow-changing frames. In the proposed algorithm, all frames of the sequence are sampled at an equal rate, which makes the encoder simpler than frame-categorized methods. We introduce a specialized...
Assessment of image similarity is fundamentally important to numerous multimedia applications. The goal of similarity assessment is to automatically assess the similarities among images in a perceptually consistent manner. In this paper, we interpret the image similarity assessment problem as an information fidelity problem. More specifically, we propose a feature-based approach to quantify the information...
We address an important issue of fully low-cost and low-complex video compression for use in resource-extremely limited sensors/devices. Conventional motion estimation-based video compression or distributed video coding (DVC) techniques all rely on the high-cost mechanism, namely, sensing/sampling and compression are disjointedly performed, resulting in unnecessary consumption of resources. That is,...
In this paper, a statistical texture modeling method is proposed for medical volumes. As the shapes of the human organ are very different from one case to another, 3D volume morphing is applied to normalize all the volume datasets to a same shape for removing shape variations. In order to deal with the problems of high-dimension and small number of medial samples, we propose an effective image compression...
The URA is one of the coded aperture imaging techniques. It has been used or proposed for X-ray imaging. In the URA, the pinhole is replaced by multi-pinholes array arranged in m-sequences. The pattern of the array is set as its correlation between the multi-pinholes and the decoding operator must be the delta function. So the URA can provide a two-dimensional image with a high resolution and high...
In this paper, we propose a new heuristic algorithm for three-dimensional image reconstruction from coded aperture images based on simulated annealing (SA). Since the coded aperture can view the object with a large solid angle, it can provide some tomographic resolution for three-dimensional object. We propose to use a heuristic algorithm to remove the defocused artifacts and to improve the tomographic...
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