Studying the influence of search rule and context shape in filtering impulse noise images with Markov chains
暂无分享,去创建一个
[1] Xinghuo Yu,et al. Adaptive progressive filter to remove impulse noise in highly corrupted color images , 2013, Signal Image Video Process..
[2] Hossein Nezamabadi-pour,et al. A Fast Adaptive Salt and Pepper Noise Reduction Method in Images , 2013, Circuits Syst. Signal Process..
[3] Ashok M. Sapkal,et al. Poisson Noise Reducing Bilateral Filter , 2016 .
[4] David A. Clausi,et al. Stochastic image denoising based on Markov-chain Monte Carlo sampling , 2011, Signal Process..
[5] David Ebenezer,et al. A New Fast and Efficient Decision-Based Algorithm for Removal of High-Density Impulse Noises , 2007, IEEE Signal Processing Letters.
[6] David Malah,et al. Non-local means denoising using a content-based search region and dissimilarity kernel , 2013, 2013 8th International Symposium on Image and Signal Processing and Analysis (ISPA).
[7] Marc-Olivier Killijian,et al. Next place prediction using mobility Markov chains , 2012, MPM '12.
[8] Raymond H. Chan,et al. Salt-and-pepper noise removal by median-type noise detectors and detail-preserving regularization , 2005, IEEE Transactions on Image Processing.
[9] Lawrence R. Rabiner,et al. A tutorial on hidden Markov models and selected applications in speech recognition , 1989, Proc. IEEE.
[10] Yicong Zhou,et al. A new weighted mean filter with a two-phase detector for removing impulse noise , 2015, Inf. Sci..
[11] Jukka Corander,et al. Bayesian clustering of DNA sequences using Markov chains and a stochastic partition model , 2014, Statistical applications in genetics and molecular biology.
[12] Heikki Mannila,et al. Random projection in dimensionality reduction: applications to image and text data , 2001, KDD '01.
[13] Chung-waHo,et al. CONVERGENCE OF NEWTON'S METHOD FOR A MINIMIZATION PROBLEM IN IMPULSE NOISE REMOVAL , 2004 .
[14] Arpad Gellert,et al. Context-based prediction filtering of impulse noise images , 2016, IET Image Process..
[15] Nor Ashidi Mat Isa,et al. Noise Adaptive Fuzzy Switching Median Filter for Salt-and-Pepper Noise Reduction , 2010, IEEE Signal Processing Letters.
[16] Rabab Kreidieh Ward,et al. Retrieving information lost by image denoising , 2015, 2015 IEEE Global Conference on Signal and Information Processing (GlobalSIP).
[17] A. Ben Hamza,et al. Removing Noise and Preserving Details with Relaxed Median Filters , 1999, Journal of Mathematical Imaging and Vision.
[18] Xun Gong,et al. Using Sorted Switching Median Filter to remove high-density impulse noises , 2013, J. Vis. Commun. Image Represent..
[19] Arpad Gellert,et al. Investigating a New Design Pattern for Efficient Implementation of Prediction Algorithms , 2013, J. Digit. Inf. Manag..
[20] Chin-Hui Lee,et al. An integrated approach to feature compensation combining particle filters and hidden Markov models for robust speech recognition , 2012, 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).
[21] Raymond H. Chan,et al. CONVERGENCE OF NEWTON'S METHOD FOR A MINIMIZATION PROBLEM IN IMPULSE NOISE REMOVAL ∗1) , 2004 .
[22] Zhou Wang,et al. Progressive switching median filter for the removal of impulse noise from highly corrupted images , 1999 .
[23] Arpad Gellert,et al. Web prefetching through efficient prediction by partial matching , 2015, World Wide Web.
[24] David J. Fleet,et al. Stochastic Image Denoising , 2009, BMVC.
[25] Bogdan Smolka,et al. Robust local similarity filter for the reduction of mixed Gaussian and impulsive noise in color digital images , 2015, Signal Image Video Process..
[26] Ondrej Krejcar,et al. Non Destructive Defect Detection by Spectral Density Analysis , 2011, Sensors.
[27] Rabab Kreidieh Ward,et al. Synthesis and analysis prior algorithms for joint-sparse recovery , 2012, 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).