Local blur estimation and super-resolution

Until now, all super-resolution algorithms have presumed that the images were taken under the same illumination conditions. This paper introduces a new approach to super-resolution, based on edge models and a local blur estimate, which circumvents these difficulties. The paper presents the theory and the experimental results using the new approach.

[1]  Shmuel Peleg,et al.  Improving image resolution using subpixel motion , 1987, Pattern Recognit. Lett..

[2]  Michal Irani,et al.  Motion Analysis for Image Enhancement: Resolution, Occlusion, and Transparency , 1993, J. Vis. Commun. Image Represent..

[3]  Roger Y. Tsai,et al.  A versatile camera calibration technique for high-accuracy 3D machine vision metrology using off-the-shelf TV cameras and lenses , 1987, IEEE J. Robotics Autom..

[4]  Andrew Blake,et al.  Motion Deblurring and Super-resolution from an Image Sequence , 1996, ECCV.

[5]  Roger Y. Tsai,et al.  Multiframe image restoration and registration , 1984 .

[6]  Ming-Chao Chiang,et al.  A public domain system for camera calibration and distortion correction , 1995 .

[7]  Ming-Chao Chiang,et al.  The Integrating Resampler and EfficientImage Warping , 1996 .

[8]  Michal Irani,et al.  Improving resolution by image registration , 1991, CVGIP Graph. Model. Image Process..

[9]  Shmuel Peleg,et al.  Image sequence enhancement using sub-pixel displacements , 1988, Proceedings CVPR '88: The Computer Society Conference on Computer Vision and Pattern Recognition.

[10]  Ming-Chao Chiang,et al.  Efficient image warping and super-resolution , 1996, Proceedings Third IEEE Workshop on Applications of Computer Vision. WACV'96.

[11]  Karl M. Fant,et al.  A Nonaliasing, Real-Time Spatial Transform Technique , 1986, IEEE Computer Graphics and Applications.

[12]  Terrance E. Boult,et al.  Local Image Reconstruction and Subpixel Restoration Algorithms , 1993, CVGIP Graph. Model. Image Process..