Two-dimensional signal and image processing
Two-dimensional signal and image processing
Digital Image Processing Using MATLAB
Digital Image Processing Using MATLAB
Unsupervised, Information-Theoretic, Adaptive Image Filtering for Image Restoration
IEEE Transactions on Pattern Analysis and Machine Intelligence
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 03
3D wavelet subbands mixing for image denoising
Journal of Biomedical Imaging
Histogram-based fuzzy filter for image restoration
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IEEE Transactions on Image Processing
A joint inter- and intrascale statistical model for Bayesian wavelet based image denoising
IEEE Transactions on Image Processing
PDE-based image restoration: a hybrid model and color image denoising
IEEE Transactions on Image Processing
Novel Cooperative Neural Fusion Algorithms for Image Restoration and Image Fusion
IEEE Transactions on Image Processing
Medical Image Noise Reduction Using the Sylvester–Lyapunov Equation
IEEE Transactions on Image Processing
Personalized identification of abdominal wall hernia meshes on computed tomography
Computer Methods and Programs in Biomedicine
| Hi-index | 0.00 |
Abstract: Reducing noise in medical images has been an important issue of research and development for medical diagnosis, patient treatment, and validation of biomedical hypotheses. Noise inherently exists in medical and biological images due to the acquisition and transmission in any imaging devices. Being different from image enhancement, the purpose of image restoration is the process of removing noise from a degraded image in order to recover as much as possible its original version. This paper presents a statistically supervised approach for medical image restoration using the concept of multiple-point geostatistics. Experimental results have shown the effectiveness of the proposed technique which has potential as a new methodology for medical and biological image processing.