Please use this identifier to cite or link to this item: http://hdl.handle.net/11455/99301
標題: A Hybrid Motion Deblurring Strategy Using Patch Based Edge Restoration and Bilateral Filter
作者: Chia-Feng Chang
Jiunn-Lin Wu
Kuan-Jen Chen
摘要: Motion blur is a common problem in digital photography. In the dim light, a long exposure time is needed to acquire a satisfactory photograph, and if the camera shakes during exposure, a motion blur is captured. Image deblurring has become a crucial image-processing challenge, because of the increased popularity of handheld cameras. Traditional motion deblurring methods assume that the blur degradation is shift-invariant; therefore, the deblurring problem can be reduced to a deconvolution problem. Edge-specific motion deblurring sharpened the strong edges of the image and then used them to estimate the blur kernel. However, this also enhanced noise and narrow edges, which cause ambiguity and ringing artifacts. We propose a hybrid-based single image motion deblurring algorithm to solve these problems. First, we separated the blurred image into strong edge parts and smooth parts. We applied the improved patch-based sharpening method to enhance the strong edge for kernel estimation, but for the smooth part, we used the bilateral filter to remove the narrow edge and the noise for avoiding the generation of ringing artifacts. Experimental results show that the proposed method is efficient at deblurring for a variety of images and can produce images of a quality comparable to other state-of-the-art techniques.
URI: http://hdl.handle.net/11455/99301
文章連結: https://link.springer.com/article/10.1007/s10851-018-0797-x
Appears in Collections:資訊科學與工程學系所

文件中的檔案:

取得全文請前往華藝線上圖書館

Show full item record
 
 
Citations:


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.