An Algorithm for Total Variation Minimization and Applications
Journal of Mathematical Imaging and Vision
Influence of the Noise Model on Level Set Active Contour Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Image Denoising Using Wavelet and Support Vector Regression
ICIG '04 Proceedings of the Third International Conference on Image and Graphics
Image Denoising Using Non-Negative Sparse Coding Shrinkage Algorithm
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Speech Enhancement Based on Hilbert-Huang Transform Theory
IMSCCS '06 Proceedings of the First International Multi-Symposiums on Computer and Computational Sciences - Volume 1 (IMSCCS'06) - Volume 01
Speech Detection Based on Hilbert-Huang Transform
IMSCCS '06 Proceedings of the First International Multi-Symposiums on Computer and Computational Sciences - Volume 1 (IMSCCS'06) - Volume 01
Adaptive fuzzy filtering for artifact reduction in compressed images and videos
IEEE Transactions on Image Processing
A new class of chromatic filters for color image processing. theory and applications
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
Partition-based vector filtering technique for suppression of noise in digital color images
IEEE Transactions on Image Processing
Edge-preserving image denoising via optimal color space projection
IEEE Transactions on Image Processing
Image Denoising Via Sparse and Redundant Representations Over Learned Dictionaries
IEEE Transactions on Image Processing
Combined spatial and temporal domain wavelet shrinkage algorithm for video denoising
IEEE Transactions on Circuits and Systems for Video Technology
Automatic RNA virus classification using the Entropy-ANFIS method
Digital Signal Processing
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A novel approach for denoising medical images is proposed based on a reconstruction-average mechanism. First, different parts of the original complete spectrum are chosen, from each of which a signal is reconstructed using a singularity function analysis (SFA) model. We finally achieve denoising by averaging these reconstructed signals using the fact that each of them is the sum of the same noise-free signal and an additive noise of varying magnitude. The theoretical ground of such approach is mathematically formulated. The experimental results on both simulated and real monochrome images show that the proposed denoising method allows efficient denoising while maintaining image quality, and presents significant advantages over conventional denoising methods.