A hybrid parallel projection approach to object-based image restoration
Pattern Recognition Letters
Example-based single document image super-resolution: a global MAP approach with outlier rejection
Multidimensional Systems and Signal Processing
Learning-Based Image Restoration for Compressed Image through Neighboring Embedding
PCM '08 Proceedings of the 9th Pacific Rim Conference on Multimedia: Advances in Multimedia Information Processing
Identification of Piecewise Linear Uniform Motion Blur
IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences
SoftCuts: a soft edge smoothness prior for color image super-resolution
IEEE Transactions on Image Processing
Learning-based image restoration for compressed images
Image Communication
A SVM-based blur identification algorithm for image restoration and resolution enhancement
KES'06 Proceedings of the 10th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part II
A VQ-Based blind super-resolution algorithm
ICIC'05 Proceedings of the 2005 international conference on Advances in Intelligent Computing - Volume Part I
Image deblurring with matrix regression and gradient evolution
Pattern Recognition
Solving the inverse problem of image zooming using "self-examples"
ICIAR'07 Proceedings of the 4th international conference on Image Analysis and Recognition
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Learning-based algorithms for image restoration and blind image restoration are proposed. Such algorithms deviate from the traditional approaches in this area, by utilizing priors that are learned from similar images. Original images and their degraded versions by the known degradation operator (restoration problem) are utilized for designing the VQ codebooks. The codevectors are designed using the blurred images. For each such vector, the high frequency information obtained from the original images is also available. During restoration, the high frequency information of a given degraded image is estimated from its low frequency information based on the codebooks. For the blind restoration problem, a number of codebooks are designed corresponding to various versions of the blurring function. Given a noisy and blurred image, one of the codebooks is chosen based on a similarity measure, therefore providing the identification of the blur. To make the restoration process computationally efficient, the principal component analysis (PCA) and VQ-nearest neighbor approaches are utilized. Simulation results are presented to demonstrate the effectiveness of the proposed algorithms.