Bidirectionally aligned sparse representation for single image super-resolution
- 1.1k Downloads
It has been demonstrated that the sparse representation based framework is one of the most popular and promising ways to handle the single image super-resolution (SISR) issue. However, due to the complexity of image degradation and inevitable existence of noise, the coding coefficients produced by imposing sparse prior only are not precise enough for faithful reconstructions. In order to overcome it, we present an improved SISR reconstruction method based on the proposed bidirectionally aligned sparse representation (BASR) model. In our model, the bidirectional similarities are first modeled and constructed to form a complementary pair of regularization terms. The raw sparse coefficients are additionally aligned to this pair of standards to restrain sparse coding noise and therefore result in better recoveries. On the basis of fast iterative shrinkage-thresholding algorithm, a well-designed mathematic implementation is introduced for solving the proposed BASR model efficiently. Thorough experimental results indicate that the proposed method performs effectively and efficiently, and outperforms many recently published baselines in terms of both objective evaluation and visual fidelity.
KeywordsSingle image super-resolution Sparse representation Sparse coefficient alignment Bidirectional similarities
The authors would like to thank the associate editor and anonymous reviewers for their constructive and precious comments, which helped us a lot in improving the presentation of this work.
This work was supported by the National Natural Science Foundation of China (No.61374194, No.61403081), the National Key Science & Technology Pillar Program of China (No.2014BAG01B03), the Key Research and Development Program of Jiangsu Province (No. BE2016739), and a Project Funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions.
- 5.Bose NK, Kim HC, Valenzuela HM (1993) Recursive implementation of total least squares algorithm for image reconstruction from noisy, undersampled multiframes. In: IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 269–272Google Scholar
- 6.Buades A, Coll B, Morel J (2005) A non-local algorithm for image denoising. In: IEEE computer society conference on Computer Vision and Pattern Recognition (CVPR), 60–65Google Scholar
- 10.Chang H, Yeung DY, Xiong Y (2004) Super-resolution through neighbor embedding. In: IEEE computer society conference on Computer Vision and Pattern Recognition (CVPR), I-IGoogle Scholar
- 13.Dong W, Zhang L, Shi G (2011) Centralized sparse representation for image restoration. In: IEEE International Conference on Computer Vision (ICCV), 1259–1266Google Scholar
- 28.Lu X, Yuan H, Yan P, Yuan Y, Li X (2012) Geometry constrained sparse coding for single image super-resolution. In: IEEE conference on Computer Vision and Pattern Recognition (CVPR), 1648–1655Google Scholar
- 39.Timofte R, De Smet V, Van Gool L (2013) Anchored neighborhood regression for fast example-based super-resolution. In: IEEE International Conference on Computer Vision (ICCV), 1920–1927Google Scholar
- 40.Timofte R, De Smet V, Van Gool L (2014) A+: adjusted anchored neighborhood regression for fast super-resolution. In: Asian Conference on Computer Vision (ACCV), 111–126Google Scholar
- 41.Timofte R, Rothe R, Van Gool L (2016) Seven ways to improve example-based single image super resolution. In: IEEE conference on Computer Vision and Pattern Recognition (CVPR), 1865–1873Google Scholar
- 42.Tsai RY, Huang TS (1984) Multiframe image restoration and registration. Advances in computer vision and Image Processing 1(2):317–339Google Scholar
- 49.Yang J, Wright J, Huang T, Ma Y (2008) Image super-resolution as sparse representation of raw image patches. In: IEEE conference on Computer Vision and Pattern Recognition (CVPR), 1–8Google Scholar
- 55.Zeyde R, Elad M, Protter M (2012) On single image scale-up using sparse-representations. Curves and Surfaces (Springer), 711–730.Google Scholar