Robust Framework For Digital Image Denoising And Deblurring
| dc.contributor.author | Toh, Kenny Kal Vin | |
| dc.date.accessioned | 2019-06-24T01:59:07Z | |
| dc.date.available | 2019-06-24T01:59:07Z | |
| dc.date.issued | 2012-06 | |
| dc.description.abstract | Image restoration concerns improving visual quality of a captured image that goes beyond the achievable limit of camera. Recent advancement in imaging and multimedia technology has advocated the interests of image restoration through software, of which applications permeate consumer photography as well as different industries. Unfortunately, the captured images often suffer from degradations, such as blurring, noise, unpleasant artifacts, and more, due to limitations of the imaging system. Despite considerable efforts have been channeled to advance the state-of-the-art methods, surprisingly, these methods are often slow and only designed for handling specific degradation model. As such, the existing methods usually fail when applied to degraded real images. Based on this motivation, a robust framework is proposed to address the main issues related to designing practical image restoration methods, namely, visual restoration quality and computational complexity. The robust framework has several advantageous properties: (1) the proposed framework is robust towards the presence of data uncertainties, (2) it is spatially adaptive to the radiometric structures of the image data, and (3) it is exceedingly robust in capturing the local structural information even in noise-ridden images. By capitalizing on the advantages of this robust framework, three novel image restoration methods have been developed in this work. The first method, termed as Augmented Variational Series and Histogram-based Clustering (AVSHC), is a switchingscheme filter that is capable to remove any kind of impulsive noise on color or monochrome images. Then, two variants based on a robust method, called Locally Adaptive Bilateral Clustering (LABC), are proposed for image denoising, mild deblurring, and sharpness enhancement. | en_US |
| dc.identifier.uri | http://hdl.handle.net/123456789/8344 | |
| dc.language.iso | en | en_US |
| dc.publisher | Universiti Sains Malaysia | en_US |
| dc.subject | Image restoration concerns improving | en_US |
| dc.subject | visual quality of a captured image | en_US |
| dc.title | Robust Framework For Digital Image Denoising And Deblurring | en_US |
| dc.type | Thesis | en_US |
Files
License bundle
1 - 1 of 1
Loading...
- Name:
- license.txt
- Size:
- 1.71 KB
- Format:
- Item-specific license agreed upon to submission
- Description: