Numerical recipes in C (2nd ed.): the art of scientific computing
Numerical recipes in C (2nd ed.): the art of scientific computing
Digital image processing
Digital Image Processing
Handbook of Image and Video Processing (Communications, Networking and Multimedia)
Handbook of Image and Video Processing (Communications, Networking and Multimedia)
Automatic Estimation and Removal of Noise from a Single Image
IEEE Transactions on Pattern Analysis and Machine Intelligence
Image denoising with an optimal threshold and neighbouring window
Pattern Recognition Letters
Gaussian Noise Removal of Image on the Local Feature
IITA '08 Proceedings of the 2008 Second International Symposium on Intelligent Information Technology Application - Volume 03
Switching bilateral filter with a texture/noise detector for universal noise removal
IEEE Transactions on Image Processing
Decoding real block codes: activity detection Wiener estimation
IEEE Transactions on Information Theory
Simple adaptive median filter for the removal of impulse noise from highly corrupted images
IEEE Transactions on Consumer Electronics
A universal noise removal algorithm with an impulse detector
IEEE Transactions on Image Processing
Robust estimation approach for blind denoising
IEEE Transactions on Image Processing
Joint Source-Channel Coding Using Real BCH Codes for Robust Image Transmission
IEEE Transactions on Image Processing
Structure-Oriented Multidirectional Wiener Filter for Denoising of Image and Video Signals
IEEE Transactions on Circuits and Systems for Video Technology
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Proper choice of denoising filter is a very important requirement for efficient image restoration because most of the filters only reduce the effect of the noise rather than removing it. In this paper, a novel algorithm for filtering of gaussian noise based on the statistics of the robust estimation is proposed. The gaussian noise is replaced by either the computed mean of the adaptively increasing localized window frame or the last processed pixel. Improved Robust Statistics are then applied to obtain the final denoised output. The proposed algorithm is objectively evaluated using Peak Signal to Noise Ratio (PSNR) as figure of merit. Simulation results indicate a marked improvement in the quality of the restored image.