On Single Image Scale-Up Using Sparse-Representations
This paper deals with the single image scale-up problem using sparse-representation modeling. The goal is to recover an original image from its blurred and down-scaled noisy version. Since this problem is highly ill-posed, a prior is needed in order to regularize it. The literature offers various ways to address this problem, ranging from simple linear space-invariant interpolation schemes (e.g., bicubic interpolation), to spatially-adaptive and non-linear filters of various sorts. We embark from a recently-proposed successful algorithm by Yang et. al. [1,2], and similarly assume a local Sparse-Land model on image patches, serving as regularization. Several important modifications to the above-mentioned solution are introduced, and are shown to lead to improved results. These modifications include a major simplification of the overall process both in terms of the computational complexity and the algorithm architecture, using a different training approach for the dictionary-pair, and introducing the ability to operate without a training-set by boot-strapping the scale-up task from the given low-resolution image. We demonstrate the results on true images, showing both visual and PSNR improvements.
KeywordsImage Patch Sparse Code Reconstruction Phase Visual Artifact Bicubic Interpolation
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- 1.Yang, J., Wright, J., Huang, T., Ma, Y.: Image super-resolution as sparse representation of raw image patches. In: IEEE Computer Vision and Pattern Recognition (CVPR) (June 2008)Google Scholar
- 2.Yang, J., Wright, J., Huang, T., Ma, Y.: Image super-resolution via sparse representation. IEEE Trans. on Image Processing (to appear)Google Scholar
- 5.Irani, M., Peleg, S.: Improving Resolution by Image Registration. CVGIP: Graphical Models and Image Processing 53, 231–239 (1991)Google Scholar
- 9.Chang, H., Yeung, D.-Y., Xiong, Y.: Super-resolution through neighbor embedding. In: IEEE Conference on Computer Vision and Pattern Classification (CVPR), vol. 1, pp. 275–282 (2004)Google Scholar
- 11.Sun, J., Xu, Z., Shum, H.: Image super-resolution using gradient profile prior. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1–8 (2008)Google Scholar
- 12.Elad, M., Aharon, M.: Image denoising via learned dictionaries and sparse representation. In: International Conference on Computer Vision and Pattern Recognition, New York, June 17-22 (2006)Google Scholar
- 16.Wang, J., Zhu, S., Gong, Y.: Resolution enhancement based on learning the sparse association of image patches. Pattern Recognition Letters 31(1) (January 2010)Google Scholar
- 17.Lou, Y., Bertozzi, A., Soatto, S.: Direct sparse deblurring. J. Math. Imag. Vis. (August 13, 2010)Google Scholar
- 21.Rubinstein, R., Zibulevsky, M., Elad, M.: Efficient Implementation of the K-SVD Algorithm using Batch Orthogonal Matching Pursuit, Technical Report - CS Technion (April 2008)Google Scholar
- 22.Glasner, D., Bagon, S., Irani, M.: Super-Resolution from a Single Image. In: International Conference on Computer Vision, ICCV (October 2009)Google Scholar