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|Title:||Noise detection and removal for highly degraded images||Authors:||Guo, Huan.||Keywords:||DRNTU::Engineering::Electrical and electronic engineering||Issue Date:||2010||Abstract:||Noise refers to any undesired signal which contaminates an image. Removal of noises from images is a critical issue in digital image processing. Image denoising is a very important processing task as a process itself or as a component of other processes. In the image processing period, the original image may become degraded in various ways. Degradation may be in the form of random noise or interference, caused by camera mis-focus and motion, lens and film nonlinearity. The most important requirement of a good image denoising model is that it can remove noise while preserving edges. This report will present a good noise detection and removal toolbox with three different filtering approaches. The toolbox developed here is able to allow user to have a good understanding of noise reduction or removal in images using linear or non-linear filtering techniques applied to different kinds of noise. User can add noise through three types: Salt and Pepper, Gaussian and Speckle. Image restoration methods usually model the degradation process and apply an approximately inverse process to the degraded image to recover the original image. To restore images corrupted by noise, various image filtering strategies have been proposed. The toolbox covered three filtering type: Median, Averaging and Adaptive Filtering.||URI:||http://hdl.handle.net/10356/42819||Rights:||Nanyang Technological University||Fulltext Permission:||restricted||Fulltext Availability:||With Fulltext|
|Appears in Collections:||EEE Student Reports (FYP/IA/PA/PI)|
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