Adaptive outlier rejection in image super-resolution
EURASIP Journal on Applied Signal Processing
Application of super-resolution image reconstruction to digital holography
EURASIP Journal on Applied Signal Processing
Subpixel edge location based on orthogonal Fourier-Mellin moments
Image and Vision Computing
Robust color image superresolution: an adaptive M-estimation framework
Journal on Image and Video Processing - Color in Image and Video Processing
Determining the regularization parameters for super-resolution problems
Signal Processing
A soft MAP framework for blind super-resolution image reconstruction
Image and Vision Computing
Region-Based Super Resolution for Video Sequences Considering Registration Error
PSIVT '09 Proceedings of the 3rd Pacific Rim Symposium on Advances in Image and Video Technology
Registration errors: are they always bad for super-resolution?
IEEE Transactions on Signal Processing
Performance of reconstruction-based super-resolution with regularization
Journal of Visual Communication and Image Representation
ISBI'10 Proceedings of the 2010 IEEE international conference on Biomedical imaging: from nano to Macro
Estimation of the parameters in regularized simultaneous super-resolution
Pattern Recognition Letters
Region-based weighted-norm with adaptive regularization for resolution enhancement
Digital Signal Processing
An iterative method for preserving edges and reducing noise in high resolution image reconstruction
ACCV'06 Proceedings of the 7th Asian conference on Computer Vision - Volume Part II
Spatially varying regularization of image sequences super-resolution
ACCV'09 Proceedings of the 9th Asian conference on Computer Vision - Volume Part III
Hierarchical blur identification from severely out-of-focus images
PSIVT'06 Proceedings of the First Pacific Rim conference on Advances in Image and Video Technology
Bayesian combination of sparse and non-sparse priors in image super resolution
Digital Signal Processing
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We propose a high-resolution image reconstruction algorithm considering inaccurate subpixel registration. A regularized iterative reconstruction algorithm is adopted to overcome the ill-posedness problem resulting from inaccurate subpixel registration. In particular, we use multichannel image reconstruction algorithms suitable for applications with multiframe environments. Since the registration error in each low-resolution image has a different pattern, the regularization parameters are determined adaptively for each channel. We propose two methods for estimating the regularization parameter automatically. The proposed algorithms are robust against registration error noise, and they do not require any prior information about the original image or the registration error process. Information needed to determine the regularization parameter and to reconstruct the image is updated at each iteration step based on the available partially reconstructed image. Experimental results indicate that the proposed algorithms outperform conventional approaches in terms of both objective measurements and visual evaluation.