The Influence of Regularization Parameter on Error Bound in Super-Resolution Reconstruction
PCM '08 Proceedings of the 9th Pacific Rim Conference on Multimedia: Advances in Multimedia Information Processing
Super-resolution without explicit subpixel motion estimation
IEEE Transactions on Image Processing
New results on performance analysis of super-resolution image reconstruction
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
IEEE Transactions on Image Processing
Performance of reconstruction-based super-resolution with regularization
Journal of Visual Communication and Image Representation
Efficient Fourier-wavelet super-resolution
IEEE Transactions on Image Processing - Special section on distributed camera networks: sensing, processing, communication, and implementation
MICCAI'10 Proceedings of the 13th international conference on Medical image computing and computer-assisted intervention: Part II
Theoretical analysis of multi-view camera arrangement and light-field super-resolution
PSIVT'11 Proceedings of the 5th Pacific Rim conference on Advances in Image and Video Technology - Volume Part I
Coordinate-descent super-resolution and registration for parametric global motion models
Journal of Visual Communication and Image Representation
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Recently, there has been a great deal of work developing super-resolution algorithms for combining a set of low-quality images to produce a set of higher quality images. Either explicitly or implicitly, such algorithms must perform the joint task of registering and fusing the low-quality image data. While many such algorithms have been proposed, very little work has addressed the performance bounds for such problems. In this paper, we analyze the performance limits from statistical first principles using Crame´r-Rao inequalities. Such analysis offers insight into the fundamental super-resolution performance bottlenecks as they relate to the subproblems of image registration, reconstruction, and image restoration.