Limits on super-resolution and how to break them
IEEE Transactions on Pattern Analysis and Machine Intelligence - Tập 24 Số 9 - Trang 1167-1183 - 2002
Tóm tắt
Nearly all super-resolution algorithms are based on the fundamental constraints that the super-resolution image should generate low resolution input images when appropriately warped and down-sampled to model the image formation process. (These reconstruction constraints are normally combined with some form of smoothness prior to regularize their solution.) We derive a sequence of analytical results which show that the reconstruction constraints provide less and less useful information as the magnification factor increases. We also validate these results empirically and show that, for large enough magnification factors, any smoothness prior leads to overly smooth results with very little high-frequency content. Next, we propose a super-resolution algorithm that uses a different kind of constraint in addition to the reconstruction constraints. The algorithm attempts to recognize local features in the low-resolution images and then enhances their resolution in an appropriate manner. We call such a super-resolution algorithm a hallucination or reconstruction algorithm. We tried our hallucination algorithm on two different data sets, frontal images of faces and printed Roman text. We obtained significantly better results than existing reconstruction-based algorithms, both qualitatively and in terms of RMS pixel error.
Từ khóa
#Image resolution #Image reconstruction #Image generation #Image analysis #Image sequence analysis #Information analysis #Algorithm design and analysis #Image recognition #Reconstruction algorithmsTài liệu tham khảo
10.1109/83.650118
10.1109/34.790425
elad, 1996, Super-Resolution Reconstruction of Image Sequences Adaptive Filtering Approach
10.1109/83.650116
10.1023/A:1026501619075
10.1109/34.93808
10.1109/83.748893
10.1016/1049-9652(91)90045-L
huang, 1984, Multi-Frame Image Restoration and Registration, Advances in Computer Vision and Image Processing, 1, 317
horn, 1996, Robot Vision
baker, 1999, Hallucinating Faces
baker, 1999, Super-Resolution Optical Flow
10.1109/AFGR.2000.840616
10.1109/CVPR.2000.854852
10.1023/A:1007901712605
barbe, 1980, Charge-Coupled Devices, 10.1007/3-540-09832-1
bascle, 1996, Motion Deblurring and Super-Resolution from an Image Sequence, Proc Fourth European Conf Computer Vision, 573
bergen, 1992, Hierarchical Model-Based Motion Estimation, Proc Second European Conf Computer Vision, 237
10.1109/CVPR.1994.323784
10.1006/jvci.1993.1030
10.1109/CVPR.1992.223272
10.1109/CVPR.1988.196317
10.1109/29.56062
10.1109/ICIP.1994.413336
10.1109/83.242363
10.1016/0030-4018(94)90210-0
nalwa, 1993, A Guided Tour of Computer Vision
10.1016/0167-8655(87)90067-5
10.1109/83.605404
10.1109/CVPR.2000.854918
10.1109/CVPR.1999.784968
pratt, 1991, Digital Image Processing
press, 1992, Numerical Recipes in C
10.1109/CVPR.1997.609311
10.1007/BF00055148
shekarforoush, 1999, Conditioning Bounds for Multi-Frame Super-Resolution Algorithms
10.1109/83.503915
10.1109/83.287017
10.1109/34.655647
10.1109/ICCV.1998.710766
10.1109/ACV.1998.732850
10.1016/1049-9652(92)90065-6
10.1109/AFGR.1998.670958
de bonet, 1997, Multiresolution Sampling Procedure for Analysis and Synthesis of Texture Images, Computer Graphics Proc Ann Conf Series (SIGGRAPH '97), 361
10.1109/CVPR.1998.698672
chiang, 1997, Imaging-Consistent Super-Resolution, Proc DARPA Image Understanding Workshop
10.1109/CVPR.1997.609422
10.1109/CVPR.2000.855843
10.1109/TCOM.1983.1095851
10.1364/JOSAA.6.001715
cheeseman, 1994, Super-Resolved Surface Reconstruction from Multiple Images
10.1016/0146-664X(81)90092-7
born, 1965, Principles of Optics