Adaptive Smoothing via Contextual and Local Discontinuities
IEEE Transactions on Pattern Analysis and Machine Intelligence
Discrete Wavelet Diffusion for Image Denoising
ICISP '08 Proceedings of the 3rd international conference on Image and Signal Processing
A Fast Scheme for Multiscale Signal Denoising
ICIAR '08 Proceedings of the 5th international conference on Image Analysis and Recognition
Performance analysis of various leaf boundary edge detection algorithms
Proceedings of the 1st Amrita ACM-W Celebration on Women in Computing in India
Image denoising by exploring the context information in the wavelet domain
ECS'10/ECCTD'10/ECCOM'10/ECCS'10 Proceedings of the European conference of systems, and European conference of circuits technology and devices, and European conference of communications, and European conference on Computer science
Adaptive non-linear diffusion in wavelet domain
ICIAR'11 Proceedings of the 8th international conference on Image analysis and recognition - Volume Part I
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We propose an iterated two-band filtering method to solve the selective image smoothing problem. We prove that a discrete computation step in an iterated nonlinear diffusion-based filtering algorithm is equivalent to a sequence of operations, including decomposition, regularization, and then reconstruction, in the proposed two-band filtering scheme. To correctly separate the high frequency components from the low frequency ones in the decomposition process, we adopt a dyadic wavelet-based approximation scheme. In the regularization process, we use a diffusivity function as a guide to retain useful data and suppress noises. Finally, the signal of the next stage, which is a "smoother" version of the signal at the previous stage, can be computed by reconstructing the decomposed low frequency component and the regularized high frequency component. Based on the proposed scheme, the smoothing operation can be applied to the correct targets. Experimental results show that our new approach is really efficient in noise removing.