CVLab COMPUTER VISION LAB

National Tsing Hua University


Image deblurring

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Sun, 21 Nov 2010 - herman
Image Deblurring by Exploting Inherent Bi-Level Regions
Po-Hao Huang, Yu-Mo Lin, Hao-Liang Yang, Shang-Hong Lai
We propose an image restoration framework for restoring an image degraded by unknown motion blur. Our approach takes advantage of inherent bi-level regions of an image to estimate a blur kernel. The framework contains three parts: bi-level region searching, initial blur kernel estimation and iterative maximum a posteriori (MAP) image restoration. Firstly, candidate bi-level regions are located around the detected corners. We use four image features to score each region and choose the best N regions for estimating an initial blur kernel. Finally, an alternating minimization algorithm is developed to iteratively refine both the blur kernel and the restored image. Experimental results of synthetic and real blurred images are shown to demonstrate the performance of the proposed algorithm.
ICIP09 Image Deblurring by Exploting Inherent Bi-Level Regions (pdf)

Medical Imaging

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Thu, 25 Nov 2010 - rily
Compensation of Motion Artifacts in MRI via Graph-Based Optimization
Tung-Ying Lee, Hong-Ren Su, Shang-Hong Lai, and Ti-chiun Chang
In two-dimensional Fourier transform magnetic resonance imaging (2DFT-MRI), patient/object motion during the image acquisition results in ghosting and blurring. These motion artifacts are commonly considered as a major limitation in the MRI community. To correct these artifacts without resorting to additional navigator echoes, most existing methods perform image quality measure to estimate motion; but they may easily fail when the motion is large. Viewed as a blind image restoration problem where the motion point spread function (PSF) is unknown, state-of-the-art restoration algorithms can not be easily applied because they cannot handle a complex PSF kernel that has the same size as the image. To overcome these challenges, we propose a novel approach that exploits the image structure to segment the kernel into several fragments. Based on this kernel representation, determining a kernel fragment can be formulated as a binary optimization problem, where each binary variable represents whether a segment in MR signals is corrupted by a certain motion or not. We establish a graphical model for these variables and estimate the kernel by minimizing an energy functional associated with the model. Experimental results show that the proposed method can provide satisfactory compensation of motion artifacts even when large motions are involved in the MR images.

Page Number Recognition

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Wed, 24 Nov 2010 - wowowo
Page Number Recognition for Reader Assistance System
曹瀠方, 葉家如, 謝涵宇, 江振國
In this project, the page number from one page of a book is identified. A webcam is attached on a table lamp. It captures the image of the current page when the reader is reading. To recognize the page number, the page area is first detected and the area of table or other irrelevant objects are removed. In the device setting, the camera doesn’t capture the image from a frontal view, a degree of 60 or 80 tilt is allowed. Thus, four corners of the book are detected to rectify the page shape. After the adjustment for lighting condition, point-set of “text” are extracted by edge detection. Lastly, a novel comparison based on distance measure for two point-sets is performed to identify the page number.