Simulation Study of the Unified Bayesian-Regularization Technique for Enhanced Radar Imaging

In this paper, we intend to present the results of extended simulation study of the family of the radar image (RI) formation algorithms that employ the recently developed and investigated fused Bayesian-regularization (FBR) paradigm for high-resolution reconstruction of the spatial spectrum pattern (SSP) of the wavefield sources distributed in the remotely sensed environment. The FBR methodology is based on the aggregation of the Bayesian minimum risk statistical optimal estimation strategy with the descriptive weighted constrained least squares optimization technique that involves the non trivial a priori information on the desired properties of the SSP to be reconstructed from the actually measured data signals. The advantages of the well designed RI experiments (that employ the FBR-based methods) over the cases of poorer designed experiments (that employ the matched spatial filtering as well as the constrained least squares estimators) are investigated trough the simulation study.Cinvesta

Neural Network Computational Technique for High-Resolution Remote Sensing Image Reconstruction with System Fusion

We address a new approach to the problem of improvement of the quality of scene images obtained with several sensing systems as required for remote sensing imagery, in which case we propose to exploit the idea of robust regularization aggregated with the neural network (NN) based computational implementation of the multi- sensor fusion tasks. Such a specific aggregated robust regularization problem is stated and solved to reach the aims of system fusion with a proper control of the NN’s design parameters (synaptic weights and bias inputs viewed as corresponding system-level and model-level degrees of freedom) which influence the overall reconstruction performances.Cinvesta

Real-Time Reconstruction of Remote Sensing Imagery: Aggregation of Robust Regularization with Neural Computing

The robustified numerical technique for real-time sensor array reconstructive image processing is developed as required for remote sensing imaging with large scale array/synthesized array radars. The addressed technique is designed via performing the regularized robustification of the fused Bayesian-regularization imaging method aggregated with the efficient real-time numerical implementation scheme that employs the neural network computing.CINVESTA

Ultrasound Imaging

In this book, we present a dozen state of the art developments for ultrasound imaging, for example, hardware implementation, transducer, beamforming, signal processing, measurement of elasticity and diagnosis. The editors would like to thank all the chapter authors, who focused on the publication of this book

Localised rank-ordered differences vector filter for suppression of high-density impulse noise in colour images

This research presents a complete study of a new alternating vector filter for the removal of impulsive noise in colour images. The method is based on an impulsive noise detector for greyscale images that has been adapted in a localised manner using geometric information for processing colour images. Based on this statistic, a filtering scheme alternating between the identity and a non-linear vector filter is proposed. A geometric and experimental study was performed to obtain the optimal filter design. Experimental studies show that the proposed technique is simple, easy to implement, robust to noise, and outperforms the classic vector filters, as well as more recent filters.Roig, B.; Estruch, V. (2016). Localised rank-ordered differences vector filter for suppression of high-density impulse noise in colour images. IET Image Processing. 10(1):24-33. doi:10.1049/iet-ipr.2014.0838S2433101Astola, J., Haavisto, P., & Neuvo, Y. (1990). Vector median filters. Proceedings of the IEEE, 78(4), 678-689. doi:10.1109/5.54807Lukac, R., & Plataniotis, K. N. (2006). A Taxonomy of Color Image Filtering and Enhancement Solutions. Advances in Imaging and Electron Physics, 187-264. doi:10.1016/s1076-5670(05)40004-xAllende, H., & Galbiati, J. (2004). A non-parametric filter for digital image restoration, using cluster analysis. Pattern Recognition Letters, 25(8), 841-847. doi:10.1016/j.patrec.2004.01.009Alajlan, N., Kamel, M., & Jernigan, E. (2004). Detail preserving impulsive noise removal. Signal Processing: Image Communication, 19(10), 993-1003. doi:10.1016/j.image.2004.08.003Pei-Eng Ng, & Kai-Kuang Ma. (2006). A switching median filter with boundary discriminative noise detection for extremely corrupted images. IEEE Transactions on Image Processing, 15(6), 1506-1516. doi:10.1109/tip.2005.871129Jin, L., & Li, D. (2007). An Efficient Color-Impulse Detector and its Application to Color Images. IEEE Signal Processing Letters, 14(6), 397-400. doi:10.1109/lsp.2006.887840Lin, T.-C., & Yu, P.-T. (2004). Partition fuzzy median filter based on fuzzy rules for image restoration. Fuzzy Sets and Systems, 147(1), 75-97. doi:10.1016/s0165-0114(03)00209-4Schulte, S., De Witte, V., Nachtegael, M., Van der Weken, D., & Kerre, E. E. (2007). Fuzzy random impulse noise reduction method. Fuzzy Sets and Systems, 158(3), 270-283. doi:10.1016/j.fss.2006.10.010Lukac, R., Plataniotis, K. N., Venetsanopoulos, A. N., & Smolka, B. (2005). A Statistically-Switched Adaptive Vector Median Filter. Journal of Intelligent and Robotic Systems, 42(4), 361-391. doi:10.1007/s10846-005-1730-2Srinivasan, K. S., & Ebenezer, D. (2007). A New Fast and Efficient Decision-Based Algorithm for Removal of High-Density Impulse Noises. IEEE Signal Processing Letters, 14(3), 189-192. doi:10.1109/lsp.2006.884018Chan, R. H., Chung-Wa, & Nikolova, M. (2005). Salt-and-pepper noise removal by median-type noise detectors and detail-preserving regularization. IEEE Transactions on Image Processing, 14(10), 1479-1485. doi:10.1109/tip.2005.852196Gallegos-Funes, F. J., & Ponomaryov, V. I. (2004). Real-time image filtering scheme based on robust estimators in presence of impulsive noise. Real-Time Imaging, 10(2), 69-80. doi:10.1016/j.rti.2004.02.002Lukac, R. (2004). Adaptive Color Image Filtering Based on Center-Weighted Vector Directional Filters. Multidimensional Systems and Signal Processing, 15(2), 169-196. doi:10.1023/b:mult.0000017024.66297.a0Morillas, S., Gregori, V., Peris-Fajarnés, G., & Latorre, P. (2005). A fast impulsive noise color image filter using fuzzy metrics. Real-Time Imaging, 11(5-6), 417-428. doi:10.1016/j.rti.2005.06.007Smolka, B., Lukac, R., Chydzinski, A., Plataniotis, K. N., & Wojciechowski, W. (2003). Fast adaptive similarity based impulsive noise reduction filter. Real-Time Imaging, 9(4), 261-276. doi:10.1016/j.rti.2003.09.015Smolka, B., & Chydzinski, A. (2005). Fast detection and impulsive noise removal in color images. Real-Time Imaging, 11(5-6), 389-402. doi:10.1016/j.rti.2005.07.003Dong, Y., & Xu, S. (2007). A New Directional Weighted Median Filter for Removal of Random-Valued Impulse Noise. IEEE Signal Processing Letters, 14(3), 193-196. doi:10.1109/lsp.2006.884014Jin, L., & Li, D. (2007). A switching vector median filter based on the CIELAB color space for color image restoration. Signal Processing, 87(6), 1345-1354. doi:10.1016/j.sigpro.2006.11.008Yuan, S.-Q., & Tan, Y.-H. (2006). Impulse noise removal by a global–local noise detector and adaptive median filter. Signal Processing, 86(8), 2123-2128. doi:10.1016/j.sigpro.2006.01.009Garnett, R., Huegerich, T., Chui, C., & Wenjie He. (2005). A universal noise removal algorithm with an impulse detector. IEEE Transactions on Image Processing, 14(11), 1747-1754. doi:10.1109/tip.2005.857261Yuzhong Shen, & Barner, K. E. (2006). Fast adaptive optimization of weighted vector median filters. IEEE Transactions on Signal Processing, 54(7), 2497-2510. doi:10.1109/tsp.2006.874028Yao Nie, & Barner, K. E. (2006). The fuzzy transformation and its applications in image processing. IEEE Transactions on Image Processing, 15(4), 910-927. doi:10.1109/tip.2005.863111Yuksel, M. E. (2006). A hybrid neuro-fuzzy filter for edge preserving restoration of images corrupted by impulse noise. IEEE Transactions on Image Processing, 15(4), 928-936. doi:10.1109/tip.2005.863941Hanji, G., & Latte, M. (2012). Detail Preserving Fast Median Based Filter. Journal of Advanced Computer Science & Technology, 1(4). doi:10.14419/jacst.v1i4.248Ibrahim, H., Pik Kong, N., & Ng, T. (2008). Simple adaptive median filter for the removal of impulse noise from highly corrupted images. IEEE Transactions on Consumer Electronics, 54(4), 1920-1927. doi:10.1109/tce.2008.4711254Perona, P., & Malik, J. (1990). Scale-space and edge detection using anisotropic diffusion. IEEE Transactions on Pattern Analysis and Machine Intelligence, 12(7), 629-639. doi:10.1109/34.56205Weickert, J. (1999). International Journal of Computer Vision, 31(2/3), 111-127. doi:10.1023/a:100800971413