EURASIP Journal on Advances in Signal Processing
A Fast Scheme for Multiscale Signal Denoising
ICIAR '08 Proceedings of the 5th international conference on Image Analysis and Recognition
Real-Time Wavelet-Spatial-Activity-Based Adaptive Video Enhancement Algorithm for FPGA
ACIVS '08 Proceedings of the 10th International Conference on Advanced Concepts for Intelligent Vision Systems
Image Denoising Using Similarities in the Time-Scale Plane
ACIVS '08 Proceedings of the 10th International Conference on Advanced Concepts for Intelligent Vision Systems
Fuzzy peer groups for reducing mixed Gaussian-impulse noise from color images
IEEE Transactions on Image Processing
A wavelet-based image denoising using least squares support vector machine
Engineering Applications of Artificial Intelligence
Chroma noise reduction in DCT domain using soft-thresholding
Journal on Image and Video Processing - Special issue on emerging methods for color image and video quality enhancement
A new fuzzy-based wavelet shrinkage image denoising technique
ACIVS'06 Proceedings of the 8th international conference on Advanced Concepts For Intelligent Vision Systems
Time-Scale Similarities for Robust Image De-noising
Journal of Mathematical Imaging and Vision
A Proposed Intelligent Denoising Technique for Spatial Video Denoising for Real-Time Applications
International Journal of Mobile Computing and Multimedia Communications
Arbitrarily shaped virtual-object based video compression
Multimedia Tools and Applications
Image denoising using SVM classification in nonsubsampled contourlet transform domain
Information Sciences: an International Journal
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A selective wavelet shrinkage algorithm for digital image denoising is presented. The performance of this method is an improvement upon other methods proposed in the literature and is algorithmically simple for large computational savings. The improved performance and computational speed of the proposed wavelet shrinkage algorithm is presented and experimentally compared with established methods. The denoising method incorporated in the proposed algorithm involves a two-threshold validation process for real-time selection of wavelet coefficients. The two-threshold criteria selects wavelet coefficients based on their absolute value, spatial regularity, and regularity across multiresolution scales. The proposed algorithm takes image features into consideration in the selection process. Statistically, most images have regular features resulting in connected subband coefficients. Therefore, the resulting subbands of wavelet transformed images in large part do not contain isolated coefficients. In the proposed algorithm, coefficients are selected due to their magnitude, and only a subset of those selected coefficients which exhibit a spatially regular behavior remain for image reconstruction. Therefore, two thresholds are used in the coefficient selection process. The first threshold is used to distinguish coefficients of large magnitude and the second is used to distinguish coefficients of spatial regularity. The performance of the proposed wavelet denoising technique is an improvement upon several other established wavelet denoising techniques, as well as being computationally efficient to facilitate real-time image-processing applications.