Digital image processing (2nd ed.)
Digital image processing (2nd ed.)
Fundamentals of digital image processing
Fundamentals of digital image processing
Scale-Space and Edge Detection Using Anisotropic Diffusion
IEEE Transactions on Pattern Analysis and Machine Intelligence
Bilateral Filtering for Gray and Color Images
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
WIAMIS '07 Proceedings of the Eight International Workshop on Image Analysis for Multimedia Interactive Services
Multivariate Statistical Models for Image Denoising in the Wavelet Domain
International Journal of Computer Vision
Digital Image Enhancement and Noise Filtering by Use of Local Statistics
IEEE Transactions on Pattern Analysis and Machine Intelligence
Bivariate shrinkage functions for wavelet-based denoising exploiting interscale dependency
IEEE Transactions on Signal Processing
Wavelet-based statistical signal processing using hidden Markovmodels
IEEE Transactions on Signal Processing
De-noising by soft-thresholding
IEEE Transactions on Information Theory
Adaptive wavelet thresholding for image denoising and compression
IEEE Transactions on Image Processing
Bayesian tree-structured image modeling using wavelet-domain hidden Markov models
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
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This paper introduces a novel circular spatial filtering scheme for suppressing additive white Gaussian noise (AWGN) under high-noise-variance conditions. In this method, a circular spatial-domain window, whose weights are derived from two independent functions: (i) spatial distance and (ii) gray level distance, is employed for filtering. The proposed filter is different from the Bilateral filter [Tomasi C, Manduchi R. Bilateral filtering for gray and color images. In: Proceedings of the IEEE internal conference on computer vision 1998. p. 836-46] and performs well under high-noise conditions. It is capable of smoothing Gaussian noise as well as retaining detailed information of images. It gives significant performance in terms of peak-signal-to-noise ratio (PSNR) and universal quality index (UQI) and outperforms many known existing spatial-domain and wavelet-domain filters. The filtered image also gives better visual quality than the existing methods.