ISSN 1000-1239 CN 11-1777/TP

Journal of Computer Research and Development ›› 2015, Vol. 52 ›› Issue (4): 943-951.doi: 10.7544/issn1000-1239.2015.20140047

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Noisy Image Super-Resolution Reconstruction Based on Sparse Representation

Dou Nuo1,2,Zhao Ruizhen1,2,3,Cen Yigang1,2,Hu Shaohai1,2, Zhang Yongdong3   

  1. 1(Institute of Information Science, Beijing Jiaotong University, Beijing 100044); 2(Beijing Key Laboratory of Advanced Information Science and Network Technology (Institute of Information Science, Beijing Jiaotong University), Beijing 100044); 3(Key Laboratory of Intelligent Information Processing, Chinese Academy of Sciences (Institute of Computing Technology, Chinese Academy of Sciences), Beijing 100190)
  • Online:2015-04-01

Abstract: Denoising and super-resolution reconstruction are performed separately in traditional methods for noisy image super-resolution reconstruction, while in the noisy image super-resolution reconstruction method based on sparse representation and dictionary learning the two processes are compounded together. Since an image patch can be well represented as a sparse linear combination of elements from an appropriately chosen over-complete dictionary, two dictionaries are trained respectively from noisy low- and clean high- resolution image patches by enforcing the similarity of two sparse representations with respect to their own dictionary. Given a noisy low-resolution image, sparse representations of low-resolution patches via trained low-dictionary are computed, then the high-resolution image can be reconstructed from high-resolution patches with the help of the related low-resolution sparse representations and trained high-dictionary, after global optimization a clean high-resolution is obtained to accomplish the goal of image super-resolution and denosing simultaneously. The experiments show that zooming low-resolution image to a middle-resolution using locally adaptive zooming algorithm for extracting features can get a better reconstructed image than bicubic interpolation algorithm. By setting the parameter λ, we can obtain the best performance both in super-resolution and denoising with absolute advantages in image quality and visual effect, which demonstrates the validity and robustness of our algorithm.

Key words: sparse representation, image super-resolution, image denoising, dictionary learning, image reconstruction

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