On ambiguities in super-resolution modeling

Some models for super-resolution restoration assume that low-resolution (LR) images are formed from high-resolution (HR) ones by a warping first, blurring second (followed by decimating) imaging process; whereas others adopt a blurring first, warping second imaging constraint. These models are abused to some extent and respective conditions for usability of them are not identified. Such ambiguities are analyzed and cleared up in this letter. Conclusions are: The warping-blurring model coincides with the general imaging physics, but it is usable only if the motion among HR images is known a priori. When the motion is estimated from LR images, this model may cause systematic error, but the use of the blurring-warping model is more appropriate, and leads to better performance.

[1]  B. Gunturk,et al.  Multiframe resolution-enhancement methods for compressed video , 2002, IEEE Signal Processing Letters.

[2]  Robert L. Stevenson,et al.  Extraction of high-resolution frames from video sequences , 1996, IEEE Trans. Image Process..

[3]  오승준 [서평]「Digital Video Processing」 , 1996 .

[4]  A. Murat Tekalp,et al.  Superresolution video reconstruction with arbitrary sampling lattices and nonzero aperture time , 1997, IEEE Trans. Image Process..

[5]  Moon Gi Kang,et al.  Super-resolution image reconstruction: a technical overview , 2003, IEEE Signal Process. Mag..

[6]  Yücel Altunbasak,et al.  Artifact reduction for set theoretic super resolution image reconstruction with edge adaptive constraints and higher-order interpolants , 2001, IEEE Trans. Image Process..

[7]  Takeo Kanade,et al.  Limits on super-resolution and how to break them , 2000, Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.PR00662).

[8]  Daniel Gross,et al.  Improved resolution from subpixel shifted pictures , 1992, CVGIP Graph. Model. Image Process..

[9]  Harpreet S. Sawhney,et al.  Is Super-Resolution with Optical Flow Feasible? , 2002, ECCV.

[10]  Shahriar Negahdaripour,et al.  Revised Definition of Optical Flow: Integration of Radiometric and Geometric Cues for Dynamic Scene Analysis , 1998, IEEE Trans. Pattern Anal. Mach. Intell..

[11]  Peyman Milanfar,et al.  A computationally efficient superresolution image reconstruction algorithm , 2001, IEEE Trans. Image Process..

[12]  Feihu Qi,et al.  Super-resolution video restoration with model uncertainties , 2002, Proceedings. International Conference on Image Processing.

[13]  Russell C. Hardie,et al.  Joint MAP registration and high-resolution image estimation using a sequence of undersampled images , 1997, IEEE Trans. Image Process..

[14]  Terrance E. Boult,et al.  Efficient super-resolution via image warping , 2000, Image Vis. Comput..

[15]  Deepu Rajan,et al.  Simultaneous Estimation of Super-Resolved Scene and Depth Map from Low Resolution Defocused Observations , 2003, IEEE Trans. Pattern Anal. Mach. Intell..

[16]  Michael Elad,et al.  Restoration of a single superresolution image from several blurred, noisy, and undersampled measured images , 1997, IEEE Trans. Image Process..

[17]  N. K. Bose,et al.  High resolution image formation from low resolution frames using Delaunay triangulation , 2002, IEEE Trans. Image Process..

[18]  Michael J. Black,et al.  A framework for the robust estimation of optical flow , 1993, 1993 (4th) International Conference on Computer Vision.

[19]  Michael Elad,et al.  A fast super-resolution reconstruction algorithm for pure translational motion and common space-invariant blur , 2001, IEEE Trans. Image Process..