DMCA
Limits on super-resolution and how to break them (2002)
Cached
Download Links
| Venue: | IEEE Transactions on Pattern Analysis and Machine Intelligence |
| Citations: | 420 - 7 self |
BibTeX
@ARTICLE{Baker02limitson,
author = {Simon Baker and Takeo Kanade},
title = {Limits on super-resolution and how to break them},
journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
year = {2002},
volume = {24},
pages = {1167--1183}
}
Share
 |
 |
 |
 |
||
OpenURL
Abstract
AbstractÐNearly all super-resolution algorithms are based on the fundamental constraints that the super-resolution image should generate the 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.) In the first part of this paper, 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 �however, many low resolution input images are used). In the second part of this paper, 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 recogstruction 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. Index TermsÐSuper-resolution, analysis of reconstruction constraints, learning, faces, text, hallucination, recogstruction. 1
Keyphrases
reconstruction constraint super-resolution algorithm second part first part large enough magnification factor appropriate manner frontal image useful information image formation process rms pixel error low resolution input image fundamental constraint reconstruction-based algorithm smoothness prior lead roman text abstract nearly index term super-resolution different data set smooth result many low resolution input image super-resolution image different kind little high-frequency content magnification factor increase hallucination algorithm analytical result low-resolution image algorithm attempt local feature
