18 January 2013 Enhancing dictionary-based super-resolution using nonlocal total variation regularization
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Abstract
The dictionary-based super-resolution (SR) method has received much attention in recent years because sparse representation is very effective for image restoration tasks. By sparse representation, original image patches are represented as a sparse linear combination of atoms in an over-complete dictionary. However, the dictionary-based SR approach has some disadvantages in that it produces annoying ringing artifacts, especially along the object boundaries and is not effective in reconstructing images that contain patterns with strong edges. We enhance the dictionary-based SR using nonlocal total variation regularization. In the training stage, we jointly train two dictionaries, Dh and D1, from the low-resolution (LR) and high-resolution (HR) training image patches by using the K-singular value decomposition (KSVD) algorithm as in conventional methods. In the synthesis stage, we obtain the sparse coefficient vector from the LR test image over the LR dictionary, and reconstruct SR patches using the coefficient vector over the HR dictionary. Then, we employ nonlocal total variation regularization to remove annoying ringing artifacts and recover the patterns with strong edges in images. Experimental results on various test images demonstrate that the proposed method is very effective in enhancing the dictionary-based SR approaches in terms of quantitative performance and visual quality.
© 2013 Society of Photo-Optical Instrumentation Engineers (SPIE)
Cheolkon Jung, Junwei Ju, Licheng Jiao, Yanru Yang, "Enhancing dictionary-based super-resolution using nonlocal total variation regularization," Optical Engineering 52(1), 017005 (18 January 2013). https://doi.org/10.1117/1.OE.52.1.017005 . Submission:
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