Scale-Space and Edge Detection Using Anisotropic Diffusion
IEEE Transactions on Pattern Analysis and Machine Intelligence
Characterization of Signals from Multiscale Edges
IEEE Transactions on Pattern Analysis and Machine Intelligence
Nonlinear total variation based noise removal algorithms
Proceedings of the eleventh annual international conference of the Center for Nonlinear Studies on Experimental mathematics : computational issues in nonlinear science: computational issues in nonlinear science
Diffusion-Inspired Shrinkage Functions and Stability Results for Wavelet Denoising
International Journal of Computer Vision
Image De-noising Algorithms Based on PDE and Wavelet
ISCID '08 Proceedings of the 2008 International Symposium on Computational Intelligence and Design - Volume 01
A four-pixel scheme for singular differential equations
Scale-Space'05 Proceedings of the 5th international conference on Scale Space and PDE Methods in Computer Vision
Fast, robust total variation-based reconstruction of noisy, blurred images
IEEE Transactions on Image Processing
A new iterated two-band diffusion equation: theory and its application
IEEE Transactions on Image Processing
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Traditional diffusivity based denoising models detect edges by the gradients of intensities, and thus are easily affected by noise. In this paper, we develop a nonlinear diffusion denoising method which adapts to the local context and thus preserves edges and diffuses more in the smooth regions. In the proposed diffusion model, the modulus of gradient in a diffusivity function is substituted by the modulus of a wavelet detail coefficient and the diffusion of wavelet coefficients is performed based on the local context. The local context is derived directly by analyzing the energy of transform across the scales and thus it performs efficiently in the real-time. The redundant representation of the stationary wavelet transform (SWT) and its shift-invariance lend themselves to edge detection and denoising applications. The proposed stationary wavelet context-based diffusivity (SWCD) model produces a better quality image compared to that attained by two high performance diffusion models, i.e. higher Peak Signal-to-Noise Ratio on average and lesser artifacts and blur are observed in a number of images representing texture, strong edges and smooth backgrounds.