Image super resolution reconstruction algorithm based on sparse representation and the UV chroma processing

Author

Cao Qi ; Guibin Zhu ; Xiaoyong Ji ; Lin Zhao

Author_Institution

Chongqing Key Lab. of Emergency Commun., Chongqing Commun. Inst., Chongqing, China

fYear

2014

fDate

16-18 May 2014

Firstpage

368

Lastpage

372

Abstract

The paper proposes an image super resolution reconstruction algorithm based on sparse representation and the UV chroma processing. For each patch of the low resolution input images, a sparse representation is sought to generate the high-resolution output. The sparse representation of a low resolution image patch can be applied to generate a high resolution image patch through dictionary learning. To further improve the effects of super resolution images, the UV chroma processing based on super resolution luminance information with bilateral filtering is put forward as well. The experimental results show the method in this paper obtains better outcomes no matter in visual effects or in the quality measures of RMSE and SSEVI.

Keywords

filtering theory; image reconstruction; image representation; image resolution; learning (artificial intelligence); UV chroma processing; bilateral filtering; dictionary learning; high resolution image patch; image superresolution reconstruction algorithm; low resolution image patch; sparse representation; superresolution luminance information; Dictionaries; Image color analysis; Image reconstruction; Image resolution; Interpolation; Reconstruction algorithms; Signal resolution; Sparse Representation; Super Resolution; UV Chroma Processing;

fLanguage

English

Publisher

ieee

Conference_Titel

Progress in Informatics and Computing (PIC), 2014 International Conference on

Conference_Location

Shanghai

Print_ISBN

978-1-4799-2033-4

Type

conf

DOI

10.1109/PIC.2014.6972359

Filename

6972359