Image Resolution Enhancement Using Wavelet Domain Transformation and Sparse Signal Representation

Image resolution enhancement or super-resolution (SR) problem generates a high resolution (HR) image from one or a set of low resolution (LR) images. In the past two decades, a wide variety of resolution enhancement algorithms have been proposed. These methods are confined to small scaling factors....

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Bibliographic Details
Published in:Procedia computer science 2016, Vol.92, p.311-316
Main Authors: Suryanarayana, Gunnam, Dhuli, Ravindra
Format: Article
Language:English
Subjects:
Discrete Wavelet Transform
Interpolation
Sparse Representation
Super Resolution
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Summary:Image resolution enhancement or super-resolution (SR) problem generates a high resolution (HR) image from one or a set of low resolution (LR) images. In the past two decades, a wide variety of resolution enhancement algorithms have been proposed. These methods are confined to small scaling factors. This paper presents a novel single image resolution enhancement algorithm in wavelet domain which operates at high scaling factors. First, we perform subband decomposition on the input LR image by using discrete wavelet transform (DWT). It decomposes the LR image into different frequency subbands namely low-low (LL), low-high (LH), high-low (HL) and high-high (HH). In parallel we apply sparse representation based interpolation method on the LR image. Next, we process the three high frequency subbands in wavelet domain by applying bicubic interpolation. Finally, the interpolated high frequency subbands in addition to the sparse recovered solution are combined to produce a HR image using inverse discrete wavelet transform (IDWT). Experiments on different LR test images demonstrate that our approach produces relatively less artifacts compared to the existing methods.
ISSN:1877-0509
1877-0509
DOI:10.1016/j.procs.2016.07.361