“Performance Analysis of Spatial and Transform Filter For Efficient Image Noise Reduction”
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Date
2015-02
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Publisher
Pulchowk Campus
Abstract
During acquisition of an image, from its source, noise becomes integral part of it,
which is very difficult to remove. Various algorithms have been used in past to
denoise images. Image denoising still has scope for improvement. Visual
information transmitted in the form of digital images is becoming a major method
of communication in the modern age, but the image obtained after transmission is
often corrupted with noise.. This thesis reviews the existing denoising algorithms,
such as filtering approach, wavelet based approach, and multiracial approach, and
performs their comparative study. Different noise models including additive and
multiplicative types are used. They include Gaussian noise, salt and pepper noise,
speckle noise and Brownian noise. Selection of the denoising algorithm is
application dependent. Hence, it is necessary to have knowledge about the noise
present in the image so as to select the appropriate denoising algorithm.. The
wavelet based approach finds applications in denoising images corrupted with
Gaussian noise. In the case where the noise characteristics are complex, the
multiracial approach can be used.. Based on using samples of degraded pixel
neighborhoods as inputs, the output of efficient filtering approach provides a
good image denoising performance which exhibited promising qualitative and
quantitative results of the degraded noisy images in terms of PSNR, MSE and
visual tests.
Description
During acquisition of an image, from its source, noise becomes integral part of it,
which is very difficult to remove.
Keywords
Image noises, Image Filtering, Wavelet transform, PSNR, MSE
Citation
Department of Electronics and Computer Engineering