Title :
Dictionary learning based multitask image restoration
Author_Institution :
Dept. of Comput. Sci., Baoji Univ. of Arts & Sci., Baoji, China
Abstract :
In recent years, there have been increased interests in the study of dictionary learning (DL). Learned dictionaries lead to state-of-the-art results in image processing. Hence, many new DL methods were presented. This paper proposes a novel DL model and an algorithm to solve this model. We call the proposed algorithm problem-guided dictionary learning (PG-DL). PG-DL can deal with many problems in image processing. Taking noised image inpainting and removing mixed noise as examples, the experiments show that the PG-DL can describe the image content effectively and leads to valid performance.
Keywords :
image restoration; learning (artificial intelligence); PG-DL; image processing; multitask image restoration; noised image inpainting; problem guided dictionary learning; Dictionaries; Gaussian noise; Image denoising; Image restoration; Joints; Dictionary learning; image inpainting; mixed noise removal; sparse representation;
Conference_Titel :
Image and Signal Processing (CISP), 2012 5th International Congress on
Conference_Location :
Chongqing
Print_ISBN :
978-1-4673-0965-3
DOI :
10.1109/CISP.2012.6469983