Abstract
A highly compressed image is usually not only of low resolution, but also suffers from compression artifacts (blocking artifact is treated as an example in this paper). Directly performing image super-resolution (SR) to a highly compressed image would also simultaneously magnify the blocking artifacts, resulting in an unpleasing visual experience. In this paper, we propose a novel learning-based framework to achieve joint single-image SR and deblocking for a highly-compressed image. We argue that individually performing deblocking and SR (i.e., deblocking followed by SR, or SR followed by deblocking) on a highly compressed image usually cannot achieve a satisfactory visual quality. In our method, we propose to learn image sparse representations for modeling the relationship between low-and high-resolution image patches in terms of the learned dictionaries for image patches with and without blocking artifacts, respectively. As a result, image SR and deblocking can be simultaneously achieved via sparse representation and morphological component analysis (MCA)-based image decomposition. Experimental results demonstrate the efficacy of the proposed algorithm.
Original language | English |
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Article number | 7109159 |
Pages (from-to) | 921-934 |
Number of pages | 14 |
Journal | IEEE Transactions on Multimedia |
Volume | 17 |
Issue number | 7 |
DOIs | |
Publication status | Published - 2015 Jul 1 |
Externally published | Yes |
Keywords
- Dictionary learning
- image decomposition
- image super-resolution
- morphological component analysis (MCA)
- self-learning
- sparse representation
ASJC Scopus subject areas
- Signal Processing
- Media Technology
- Computer Science Applications
- Electrical and Electronic Engineering