Least-Squares Estimation of Transformation Parameters Between Two Point Patterns
IEEE Transactions on Pattern Analysis and Machine Intelligence
Alignment by Maximization of Mutual Information
International Journal of Computer Vision
Interpolation artefacts in mutual information-based image registration
Computer Vision and Image Understanding - Special issue on analysis of volumetric image
Motion Deblurring and Super-resolution from an Image Sequence
ECCV '96 Proceedings of the 4th European Conference on Computer Vision-Volume II - Volume II
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Mutual Information Regularized Bayesian Framework for Multiple Image Restoration
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
Resolution enhancement via probabilistic deconvolution of multiple degraded images
Pattern Recognition Letters - Special issue: Pattern recognition in remote sensing (PRRS 2004)
Optical flow based super-resolution: A probabilistic approach
Computer Vision and Image Understanding
A frequency domain approach to registration of aliased images with application to super-resolution
EURASIP Journal on Applied Signal Processing
Overcoming registration uncertainty in image super-resolution: maximize or marginalize?
EURASIP Journal on Advances in Signal Processing
An iterative image registration technique with an application to stereo vision
IJCAI'81 Proceedings of the 7th international joint conference on Artificial intelligence - Volume 2
IEEE Transactions on Image Processing
A Mutual Information Based Sub-Pixel Registration Method for Image Super Resolution
IIH-MSP '09 Proceedings of the 2009 Fifth International Conference on Intelligent Information Hiding and Multimedia Signal Processing
Edge-preserving Bayesian image superresolution based on compound Markov random fields
ICANN'07 Proceedings of the 17th international conference on Artificial neural networks
SURF: speeded up robust features
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
Simultaneous motion estimation and segmentation
IEEE Transactions on Image Processing
Robust, object-based high-resolution image reconstruction from low-resolution video
IEEE Transactions on Image Processing
Joint MAP registration and high-resolution image estimation using a sequence of undersampled images
IEEE Transactions on Image Processing
Image quality assessment: from error visibility to structural similarity
IEEE Transactions on Image Processing
Bayesian resolution enhancement of compressed video
IEEE Transactions on Image Processing
Multiframe demosaicing and super-resolution of color images
IEEE Transactions on Image Processing
Image information and visual quality
IEEE Transactions on Image Processing
An image super-resolution algorithm for different error levels per frame
IEEE Transactions on Image Processing
Super-Resolution Based on Fast Registration and Maximum a Posteriori Reconstruction
IEEE Transactions on Image Processing
Simultaneous recursive displacement estimation and restoration of noisy-blurred image sequences
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
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The accuracy of image registration plays a dominant role in image super-resolution methods and in the related literature, landmark-based registration methods have gained increasing acceptance in this framework. In this work, we take advantage of a maximum a posteriori (MAP) scheme for image super-resolution in conjunction with the maximization of mutual information to improve image registration for super-resolution imaging. Local as well as global motion in the low-resolution images is considered. The overall scheme consists of two steps. At first, the low-resolution images are registered by establishing correspondences between image features. The second step is to fine-tune the registration parameters along with the high-resolution image estimation, using the maximization of mutual information criterion. Quantitative and qualitative results are reported indicating the effectiveness of the proposed scheme, which is evaluated with different image features and MAP image super-resolution computation methods.