Title :
PSF Recovery from Examples for Blind Super-Resolution
Author :
Bégin, Isabelle ; Ferrie, Frank P.
fDate :
Sept. 16 2007-Oct. 19 2007
Abstract :
This paper addresses the problem of super-resolving a single image and recovering the characteristics of the sensor using a learning-based approach. In particular, the point spread function (PSF) of the camera is sought by minimizing the mean Euclidean distance function between patches from the input frame and from degraded versions of high-resolution training images. Once an estimate of the PSF is obtained, a supervised learning algorithm can then be used as is. Results are compared with another method for blind super-resolution by using a series of quality measures.
Keywords :
estimation theory; image resolution; image sensors; learning (artificial intelligence); blind super image resolution; camera; image sensor; mean Euclidean distance function; point spread function estimation; supervised learning algorithm; Belief propagation; Cameras; Degradation; Euclidean distance; Image databases; Image quality; Image resolution; Markov random fields; Signal resolution; Supervised learning; Image Quality; Learning; Markov Random Fields; Point Spread Function; Super-Resolution;
Conference_Titel :
Image Processing, 2007. ICIP 2007. IEEE International Conference on
Conference_Location :
San Antonio, TX
Print_ISBN :
978-1-4244-1437-6
Electronic_ISBN :
1522-4880
DOI :
10.1109/ICIP.2007.4379855
