TY - JOUR
T1 - SoftCuts
T2 - A soft edge smoothness prior for color image super-resolution
AU - Dai, Shengyang
AU - Han, Mei
AU - Xu, Wei
AU - Wu, Ying
AU - Gong, Yihong
AU - Katsaggelos, Aggelos K.
N1 - Funding Information:
Manuscript received June 14, 2008; revised November 10, 2008. Current version published April 10, 2009. This work was supported in part by National Science Foundation Grant IIS-0347877. The associate editor coordinating the review of this manuscript and approving it for publication was Dr. Pier Luigi Dragotti.
Funding Information:
Yihong Gong received the B.S., M.S., and Ph.D. de-grees in electronic engineering from the University of Tokyo, Tokyo, Japan, in 1987, 1989, and 1992, re-spectively. He joined the Nanyang Technological University (NTU), Singapore, where he was an Assistant Professor at the School of Electrical and Electronic Engineering. Between June 1996 and December 1998, he was with the Robotics Institute, Carnegie Mellon University, Pittsburgh, PA, as a project sci-entist and principal member of both the Informedia Digital Video Library project and the Experience-On-Demand project funded by NSF, DARPA, and other governmental agencies. In 1999, he joined NEC Laboratories America, Cupertino, CA, where he built the multimedia content analysis team from scratch. In 2006, he became Head of the silicon valley branch of the labs. His research interests include computer vision, multimedia content analysis, and machine learning.
PY - 2009
Y1 - 2009
N2 - Designing effective image priors is of great interest to image super-resolution (SR), which is a severely under-determined problem. An edge smoothness prior is favored since it is able to suppress the jagged edge artifact effectively. However, for soft image edges with gradual intensity transitions, it is generally difficult to obtain analytical forms for evaluating their smoothness. This paper characterizes soft edge smoothness based on a novel SoftCuts metric by generalizing the Geocuts method [1]. The proposed soft edge smoothness measure can approximate the average length of all level lines in an intensity image. Thus, the total length of all level lines can be minimized effectively by integrating this new form of prior. In addition, this paper presents a novel combination of this soft edge smoothness prior and the alpha matting technique for color image SR, by adaptively normalizing image edges according to their α-channel description. This leads to the adaptive SoftCuts algorithm, which represents a unified treatment of edges with different contrasts and scales. Experimental results are presented which demonstrate the effectiveness of the proposed method.
AB - Designing effective image priors is of great interest to image super-resolution (SR), which is a severely under-determined problem. An edge smoothness prior is favored since it is able to suppress the jagged edge artifact effectively. However, for soft image edges with gradual intensity transitions, it is generally difficult to obtain analytical forms for evaluating their smoothness. This paper characterizes soft edge smoothness based on a novel SoftCuts metric by generalizing the Geocuts method [1]. The proposed soft edge smoothness measure can approximate the average length of all level lines in an intensity image. Thus, the total length of all level lines can be minimized effectively by integrating this new form of prior. In addition, this paper presents a novel combination of this soft edge smoothness prior and the alpha matting technique for color image SR, by adaptively normalizing image edges according to their α-channel description. This leads to the adaptive SoftCuts algorithm, which represents a unified treatment of edges with different contrasts and scales. Experimental results are presented which demonstrate the effectiveness of the proposed method.
KW - Edge smoothness
KW - SoftCuts
KW - Super-resolution (SR)
KW - α-channel description
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U2 - 10.1109/TIP.2009.2012908
DO - 10.1109/TIP.2009.2012908
M3 - Article
C2 - 19342335
AN - SCOPUS:65149092220
VL - 18
SP - 969
EP - 981
JO - IEEE Transactions on Image Processing
JF - IEEE Transactions on Image Processing
SN - 1057-7149
IS - 5
ER -