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Wavelet-based Image Sharpening and De-blurring, Slides of Digital Image Processing
A wavelet-based approach for image sharpening and de-blurring. The method uses wavelet coefficients to provide multi-resolution high-frequency components of an image, identifies noise-related coefficients through correlation between sub-bands, and obtains the correction image by taking the inverse discrete wavelet transform (IDWT) of the selected coefficients. The document also covers image de-blurring, which includes the blurring process, de-blurring model, edge detection, and kernel estimation.
Typology: Slides
2019/2020
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A wavelet based approach for
image sharpening
- (^) Wavelet coefficients provide multi-resolution high
frequency components of an image.
Identifying noise-related coefficients using Correlation between the sub-bands Figure depicting correlation Comparing the related coefficients
Y(m,n)=X(m,n)+λ*Z(m,n) Obtaining the correction image
- After taking IDWT of the selected co-efficients we obtain the correction image
De-blurring model
Length of the PSF Edge detection Estimation of the Angle of PSF De-blurred Image Gradient detection Estimated Kernel Deconvolution Blurred image
Kernel(PSF) estimation:
Estimation of angle of motion: Estimation of length of motion: