Summary
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Joint Blind Super-Resolution and Shadow Removing Jianping QIAO Ju LIU Yen-Wei CHEN Publication IEICE TRANSACTIONS on Information and Systems Vol.E90-D No.12 pp.2060-2069 Publication Date: 2007/12/01 Online ISSN: 1745-1361 DOI: 10.1093/ietisy/e90-d.12.2060 Print ISSN: 0916-8532 Type of Manuscript: PAPER Category: Image Processing and Video Processing Keyword: single-frame super-resolution, support vector machines, vector quantization, hidden Markov model, logarithmic-wavelet transform, shadow removal, Full Text: PDF(1.1MB)>>
Summary: Most learning-based super-resolution methods neglect the illumination problem. In this paper we propose a novel method to combine blind single-frame super-resolution and shadow removal into a single operation. Firstly, from the pattern recognition viewpoint, blur identification is considered as a classification problem. We describe three methods which are respectively based on Vector Quantization (VQ), Hidden Markov Model (HMM) and Support Vector Machines (SVM) to identify the blur parameter of the acquisition system from the compressed/uncompressed low-resolution image. Secondly, after blur identification, a super-resolution image is reconstructed by a learning-based method. In this method, Logarithmic-wavelet transform is defined for illumination-free feature extraction. Then an initial estimation is obtained based on the assumption that small patches in low-resolution space and patches in high-resolution space share a similar local manifold structure. The unknown high-resolution image is reconstructed by projecting the intermediate result into general reconstruction constraints. The proposed method simultaneously achieves blind single-frame super-resolution and image enhancement especially shadow removal. Experimental results demonstrate the effectiveness and robustness of our method. | ||||||||||
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