Mixed Gaussian and uniform impulse noise analysis using robust estimation for digital images
DSP'09 Proceedings of the 16th international conference on Digital Signal Processing
A low-cost VLSI implementation for efficient removal of impulse noise
IEEE Transactions on Very Large Scale Integration (VLSI) Systems
Switching bilateral filter with a texture/noise detector for universal noise removal
IEEE Transactions on Image Processing
Impulse noise filtering using robust pixel-wise S-estimate of variance
EURASIP Journal on Advances in Signal Processing - Special issue on robust processing of nonstationary signals
Optimal depth estimation by combining focus measures using genetic programming
Information Sciences: an International Journal
Restoration of embedded image from corrupted stego image
Signal Processing
Information Sciences: an International Journal
Genetic programming based blind image deconvolution for surveillancesystems
Engineering Applications of Artificial Intelligence
Modified directional weighted filter for removal of salt & pepper noise
Pattern Recognition Letters
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In this paper, we present a novel method for impulse noise filter construction, based on the switching scheme with two cascaded detectors and two corresponding estimators. Genetic programming as a supervised learning algorithm is employed for building two detectors with complementary characteristics. The first detector identifies the majority of noisy pixels. The second detector searches for the remaining noise missed by the first detector, usually hidden in image details or with amplitudes close to its local neighborhood. Both detectors are based on the robust estimators of location and scale-median and MAD. The filter made by the proposed method is capable of effectively suppressing all kinds of impulse noise, in contrast to many existing filters which are specialized only for a particular noise model. In addition, we propose the usage of a new impulse noise model-the mixed impulse noise, which is more realistic and harder to treat than existing impulse noise models. The proposed model is the combination of commonly used noise models: salt-and-pepper and uniform impulse noise models. Simulation results show that the proposed two-stage GP filter produces excellent results and outperforms existing state-of-the-art filters.