title: Efficient blind image restoration using discrete periodic radon transform.
abstract: Restoring an image from its convolution with an unknown blur function is a well-known ill-posed problem in image processing. Many approaches have been proposed to solve the problem and they have shown to have good performance in identifying the blur function and restoring the original image. However, in actual implementation, various problems incurred due to the large data size and long computational time of these approaches are undesirable even with the current computing machines. In this paper, an efficient algorithm is proposed for blind image restoration based on the discrete periodic Radon transform (DPRT). With DPRT, the original two-dimensional blind image restoration problem is converted into one-dimensional ones, which greatly reduces the memory size and computational time required. Experimental results show that the resulting approach is faster in almost an order of magnitude as compared with the traditional approach, while the quality of the restored image is similar.
authors.affiliation: Centre for Multimedia Signal Processing, Department of Electronic and Information Engineering, The Hong Kong Polytechnic University, Kowloon, Hong Kong. enpklun@polyu.edu.hk
authors.forename: Daniel P K
authors.initials: DP
authors.lastname: Lun
authors.forename: Tommy C L
authors.initials: TC
authors.lastname: Chan
authors.forename: Tai-Chiu
authors.initials: TC
authors.lastname: Hsung
authors.forename: David Dagan
authors.initials: DD
authors.lastname: Feng
authors.forename: Yuk-Hee
authors.initials: YH
authors.lastname: Chan
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D016247:Information Storage and Retrieval / Q000379:methods*; D015233:Models, Statistical; D010507:Periodicity; D011786:Quality Control; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012815:Signal Processing, Computer-Assisted; D013269:Stochastic Processes