Improving resolution by image registration
CVGIP: Graphical Models and Image Processing
An MRF-Based Approach to Generation of Super-Resolution Images from Blurred Observations
Journal of Mathematical Imaging and Vision
Video Processing and Communications
Video Processing and Communications
Computer Methods for Mathematical Computations
Computer Methods for Mathematical Computations
Digital Image Restoration
FG '00 Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition 2000
ICIP '95 Proceedings of the 1995 International Conference on Image Processing (Vol.2)-Volume 2 - Volume 2
Blind Super-Resolution Using a Learning-Based Approach
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 2 - Volume 02
A frequency domain approach to registration of aliased images with application to super-resolution
EURASIP Journal on Applied Signal Processing
Extraction of high-resolution frames from video sequences
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
Total variation blind deconvolution
IEEE Transactions on Image Processing
A computationally efficient superresolution image reconstruction algorithm
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
Extension of phase correlation to subpixel registration
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
Multichannel blind iterative image restoration
IEEE Transactions on Image Processing
Fast and robust multiframe super resolution
IEEE Transactions on Image Processing
Multichannel blind deconvolution of spatially misaligned images
IEEE Transactions on Image Processing
An image super-resolution algorithm for different error levels per frame
IEEE Transactions on Image Processing
A super-resolution reconstruction algorithm for surveillance images
Signal Processing
Stochastic super-resolution image reconstruction
Journal of Visual Communication and Image Representation
A closed form algorithm for superresolution
ISVC'11 Proceedings of the 7th international conference on Advances in visual computing - Volume Part II
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This paper proposes a new algorithm to address blind image super-resolution (SR) by fusing multiple low-resolution (LR) blurred images to render a high-resolution (HR) image. Conventional SR image reconstruction algorithms assume the blurring occurred during the image formation process to be either negligible or can be characterized fully a priori. This assumption, however, is impractical as it is often difficult to eliminate the blurring completely in some applications or to know the blurring function completely a priori. In view of this, we present a new soft maximum a posteriori (MAP) estimation framework to perform joint blur identification and HR image reconstruction. The proposed method incorporates a soft blur prior that estimates the relevance of the best-fit parametric blur model, and induces reinforcement learning towards it. An iterative scheme based on alternating minimization is developed to estimate the blur and the HR image progressively. Experimental results show that the new method is effective in performing blind SR image reconstruction where there is limited information about the blurring function.