Tham khảo tài liệu 'recent advances in signal processing 2011 part 8', kỹ thuật - công nghệ, cơ khí - chế tạo máy phục vụ nhu cầu học tập, nghiên cứu và làm việc hiệu quả | 232 Recent Advances in Signal Processing Fig. 16. Denoising results of Goldhill image corrupted by heavily correlated streak noise top left by NLMS denoising top right by BLS-GSM denoising bottom left by Probshrink denoising for white noise bottom right In a fourth denoising experiment the Stonehenge image was used. It was treated as a color image and used as input for a mosaicing demosaicing experiment using the bilinear demosaicing algorithm. This results in low frequency noise structures. Then the red channel of the resulting color image was used as input for the denoising experiment. Again it is visible that the white noise denoising algorithm Probshrink does not succeed in suppressing the noise artifacts while the algorithms for correlated noise do. It is also visible that the BLS-GSM algorithm suffers from ringing near the top edge of the Stonehenge structure. This type of artifacts is common in wavelet-base denoising experiments and is a result from incorrectly suppressing the small coefficients that make up the edge in higher frequency scales while keeping their respective counterparts in lower frequency scales. Suppression of Correlated Noise 233 Fig. 17. Denoising results of Stonehenge image corrupted by simulated red channel demosaicing noise top left by NLMS denoising top right by BLS-GSM denoising bottom left by Probshrink denoising for white noise bottom right From the experiments some conclusions can be made. White noise denoising algorithms such as Probshrink work well enough as long as the image is corrupted by white noise. It fails when presented with correlated noise. One reason is that the Donoho MAD estimator is often a very bad choice leading to underestimated noise power for low frequency noise or severely overestimated noise power for high frequency noise . Because of this failure of the MAD estimator the choice was made to choose the noise variance parameter heuristically for the white noise Probshrink algorithm in order to obtain the .
