Abstract:
Super-resolution is usually posed as a reconstruction problem. The low resolution input images are assumed to be noisy, downsampled versions of an unknown super-resolution image that is to be estimated. A common way of inverting the down-sampling process is to write down the reconstruction constraints and then solve them, often adding a smoothness prior to regularize the solution. In this paper, we present two results which both show that there is more to super-resolution than image reconstruction. We first analyze the reconstruction constraints and show that they provide less and less useful information as the magnification factor increases. Afterwards, we describe a “hallucination ” algorithm, incorporating the recognition of local features in the low resolution images, which outperforms existing reconstruction-based algorithms. 1.
Citations
|
212
|
Learning low-level vision
– Freeman, Pasztor, et al.
- 2000
|
|
166
|
Multiresolution sampling procedure for analysis and synthesis of textured images
– BONET
- 1997
|
|
126
|
Limits on super-resolution and how to break them
– Baker, Kanade
|
|
122
|
Restoration of a single superresolution image from several blurred, noisy, and undersampled measured images
– Elad, Feuer
- 1997
|
|
122
|
Extraction of highresolution frames from video sequences
– Schultz, Stevenson
- 1996
|
|
88
|
Multiframe image restoration and registration
– Tsai, Huang
- 1984
|
|
85
|
Joint MAP registration and high-resolution image estimation using a sequence of undersampled images
– Hardie, Barnard, et al.
- 1997
|
|
62
|
Superresolved surface reconstruction from multiple Images
– Cheeseman, Kanefsky, et al.
- 1994
|
|
61
|
Parametric feature detection
– Baker, Nayar, et al.
- 1998
|
|
49
|
Learning to Identify and Track Faces in Image Sequences
– Edwards, Taylor, et al.
- 1997
|
|
44
|
The feret evaluation methodology for face-recognition algorithms
– Philips, Moon, et al.
- 2000
|
|
30
|
Hallucinating faces
– Baker, Kanade
- 2000
|
|
30
|
Improving image resolution using subpixel motion
– Peleg, Keren, et al.
- 2000
|
|
18
|
The Quotient image: Class based recognition and synthesis under varying illumination
– Riklin-Raviv, Shashua
- 1999
|
