Pham Cong Thang
ABSTRACT
In this work, we present procedures for image denoising based on dynamic programming procedure for maximum a posteriori probability estimation. A new non-convex type regularization is used, with ability to flexibly set a priori preferences, using different penalties for various ranges of differences between the values of adjacent image elements. Proposed procedures can take into account heterogeneities and discontinuities in the source data.
- Rudin, L., Osher, S., and Fatemi, E. (1992). Nonlinear total variation based noise removal algorithms. In: Journal Physica D, Vol. 60, pp. 259--268. Google Scholar
Digital Library
- Chambolle A., Caselles V., and Novaga M. (2011), Total Variation in Imaging, Handbook of Mathematical Methods in Imaging, Springer, New York., 1016-57.Google Scholar
- Chaudhury, K., Sage D. and Unser, M. Fast O(1) bilateral filtering using trigonometric range kernels, 2011). In: IEEE Transactions on Image Processing, Vol. 20(12), pp. 3376--3382. Google ScholarDigital Library
- Florian Luisier, Thierry Blu, Michael Unser, 2007. A New SURE Approach to Image Denoising: Interscale Orthonormal Wavelet Thresholding. IEEE Transactions on Image Processing, vol. 16(3): p. 593---606. Google ScholarDigital Library
- Portilla, J., et al., 2003. Image denoising using scale mixtures of gaussians in the wavelet domain. In: IEEE Transactions On Image Processing, Vol. 12, pp. 1338--1351. Google ScholarDigital Library
- Buades, A., Coll, B., Morel, J.M. (2005). A review of image denoising algorithms with a new one. Multiscale Modeling and Simulation, 4 (2), 490--530.Google ScholarCross Ref
- Hammersley J. M., Clifford P.E., Markov random fields on finite graphs and lattices, 1971.Google Scholar
- Mottl V., et al. (1998). Optimization techniques on pixel neighborhood graphs for image processing. In: Graph-Based Representations in Pattern Recognition. Computing, Supplement 12. Springer-Verlag/Wien, pp. 135--145.Google ScholarCross Ref
- Blake A., Zisserman A. (1987). Visual Reconstruction. MIT Press, Cambridge, Massachusetts, 1987, 232 pages. Google ScholarCross Ref
- Ramin Zabih, et al., (2008), A comparative study of energy minimization methods for Markov Random Fields with smoothness-based priors, Pattern Analysis and Machine Intelligence, IEEE Transactions, Vol.30 (6), pp. 1068--1080. Google ScholarDigital Library
- Nikolova M., Michael K., and Tam C.P. (2010), Fast Nonconvex Nonsmooth Minimization Methods for Image Restoration and Reconstruction. In: IEEE Transactions on Image Processing, Vol. 19, no. 12, pp. 3073--3088. Google ScholarDigital Library
- Pham C. T. and Kopylov A. V. (2015), Multi-Quadratic Dynamic Programming Procedure of Edge-Preserving Denoising for Medical Images, Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-5/W6, (2015), pp. 101--06.Google ScholarCross Ref
- Stevenson R., Stevenson D.E. (1990), Fitting curves with discontinuities. Proc. of the first international workshop on robust computer vision, pp. 127--136.Google Scholar
- Fleury G., De la Rosa J. I. (2004), Bootstrap Methods for a Measurement Estimation Problem // IEEE Transactions on Instrumentation and Measurement, Vol. 55(3). P. 820--827.Google Scholar
- Bouman, C., Sauer K. (1993), A Generalized Gaussian Image Model for Edge-Preserving Map Estimation, IEEE Transactions on Image Processing, Vol. 2 (3). P. 296--310. Google ScholarDigital Library
- Besag J. (1986) On the Statistical Analysis of Dirty Pictures, ournal of the Royal Statistical Society (Series B), Vol. 48, P. 259--302.Google Scholar
- Kopylov A., et al. (2010), A Signal Processing Algorithm Based on Parametric Dynamic Programming, Lecture Notes in Computer Science, Volume 6134 (2010). P.280--86. Google ScholarDigital Library
- Kopylov A.V., Parametric dynamic programming procedures for edge preserving in smoothing of signals and images (2005), Pattern recognition and image analysis,15,: p. 227--229.Google Scholar
- Kalman R. E., Bucy R.S., (1961) New Results in Linear Filtering and Prediction Theory. Journal of Basic Engineering, Vol. 83. P. 95--108.Google ScholarCross Ref
- Dvoenko S. D. (2009) Clustering Sets Based on Distances and Proximities between Its Elements, Sib. Zh. Ind. Mat., 12:1 (2009), 61--73 (In Russian).Google Scholar
- Bovik A. C., Wang Z. (2006), Modern Image Quality Assessment, Synthesis Lectures on Image, Video, and Multimedia Processing, Morgan & Claypool Publishers (2006), 156 pages. Google ScholarDigital Library
- Kopylov A.V. Dynamic programming procedures for image analysis // Proceedings of the Eight IASTED International Conference Intelligent systems and control, Cambridge, USA, ACTA Press. (2005). P. 404--409.Google Scholar
Index Terms
- Parametric procedures for image denoising with flexible prior model
Recommendations
Nonlocal image denoising via adaptive tensor nuclear norm minimization
Nonlocal self-similarity shows great potential in image denoising. Therefore, the denoising performance can be attained by accurately exploiting the nonlocal prior. In this paper, we model nonlocal similar patches through the multi-linear approach and ...
A New Nonlocal H1 Model for Image Denoising
Following ideas of Kindermann et al. (Multiscale Model. Simul. 4(4):1091---1115, 2005 ) and Gilboa and Osher (Multiscale Model. Simul. 7:1005---1028, 2008 ) we introduce new nonlocal operators to interpret the nonlocal means filter (NLM) as a ...
Improved Image Compressive Sensing Recovery with Low-Rank Prior and Deep Image Prior
Highlights- We propose a novel patch-based prior, namely non-convex low-rank (NCLR) prior established from weighted Schatten p-norm. By plugging FFDNet-based deep prior ...
AbstractCompressive sensing (CS) aims to recover images with rich and accurate information from a small amount of sampled data. Due to its ill-posedness, the model-based CS method has been widely used ever. In recent years, with the ...
Comments