A new approach for processing images based on joint application of the residue number system and finite-field wavelets is proposed in this work. It is shown that the residue number system with simple modules can be used for implementation of the digital processing of signals using finite-field wavelets. The principle of organization of computations in the wavelet transform of images in the residue number system is proposed. There are given examples showing how this method can be used for compression of images and protection of confidential information.

Digital filtering of images in a residue number system using finite-field wavelets

The multiframe image super-resolution (SR) has appeal great care due to its relevant practical applications. Since the SR methods are always considered as ill-posed problems, great interest is therefore investigated to reconstruct an image with less artefacts. In this paper, we propose a high-order partial differential equation (PDE) that preserve singularities and edge continuities while smoothing. This PDE is based on anisotropic diffusion behavior by adding some constraints to the Weickert coherence enhancing diffusion tensor in order to control the diffusion process near edges and avoids tiny corners destruction. In fact, this PDE combine between the diffusion process of Perona–Malik in the flat regions and the anisotropic diffusion of the Weickert model near strong edges. Moreover, the introduction of the high-order derivatives reduces unwanted artifacts such as staircasing. The given experimental results show the effectiveness of the proposed PDE compared to some competitive methods in both visually and quantitatively.

An edge preserving high-order PDE for multiframe image super-resolution

Micro-electro-mechanical systems (MEMS)-based cross-connects are widely used for all-optical switching in recent optical networks. This paper provides a brief overview of various photonic switching technologies and a detailed review of the working principles, actuating mechanisms, and architectures of MEMS-based optical cross-connects (OXCs) from the last two decades. New approaches, which, potentially, will be adopted in the next generation of MEMS-based cross-connects are also described. The switching MEMS-based cross-connects speed can vary from less than 1 ${\mu }\text{s}$ to tens of ms depending on the chosen switching mechanism and actuator parameters. This paper describes the various types of actuators used to manipulate the optical signal inside all-OXCs. Optical properties (insertion loss, crosstalk), switching speed, and scalability are also discussed.

A Comparative Review of MEMS-Based Optical Cross-Connects for All-Optical Networks From the Past to the Present Day

All-optical switching networks have been considered a promising candidate for the next generation data center networks thanks to its scalability in data bandwidth and power efficiency. However, the bufferless nature and the nonzero reconfiguration delay of optical switches remain great challenges in deploying all-optical networks. This paper considers the end-to-end scheduling for all-optical data center networks with no in-network buffer and nonzero reconfiguration delay. A framework is proposed to deal with the nonzero reconfiguration delay. The proposed approach constructs an adaptive variant of any given scheduling policy. It is shown that if a scheduling policy guarantees its schedules to have schedule weights close to the MaxWeight schedule (and thus is throughput optimal in the zero reconfiguration regime), then the throughput optimality is inherited by its adaptive variant (in any nonzero reconfiguration delay regime). As a corollary, a class of adaptive variants of the well-known MaxWeight policy is shown to achieve throughput optimality without prior knowledge of the traffic load. Furthermore, through numerical simulations, the simplest such policy, namely, the Adaptive MaxWeight, is shown to exhibit better delay performance than all prior work.

Adaptive Policies for Scheduling With Reconfiguration Delay: An End-to-End Solution for All-Optical Data Centers

Development and improvement of a mathematical model for a large-scale analysis based on the Daubechies discrete wavelet transform will be implemented in an algebraic system possessing a property of ring and field suitable for speech signals processing. Modular codes are widely used in many areas of modern information technologies. The use of these non-positional codes can provide high-speed data processing. Therefore, these algebraic systems should be used in the algorithms of digital processing of signals, which are characterized by processing large amounts of data in real time. In addition, modular codes make it possible to implement large-scale signal processing using the wavelet transform. The paper discusses examples of the Daubechies wavelet transform application. Integer processing, presented in the paper, will reduce the number of rounding errors when processing the speech signals.

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An Algorithm of Daubechies Wavelet Transform in the Final Field When Processing Speech Signals

Compressive Sensing (CS) is known as a new sampling theory that use small number of basis elements for constructing of signals (or images) and these basis elements (so called sparsity number) are very important parameters that approximate how sparsify the image is. Due to several characteristics of each image groups, the sparsity number could be varied and there is unfortunately very little research for this issue. This paper presents two main contributions: First, this paper proposes a practical sparsity number estimation technique using for an image reconstruction for SL0 algorithm based on Discrete Cosine Transform domain (DCT). Second the practical sparsity number of difference image groups is the experiment based on over 2000 images. The DCT is exclusively applied for a sparse representation of images because it is proven as a useful instrument for image analysis and processing. In general, images can be represented by a linear superposition of small number of wavelet elements selected from a suitable filter. The proposed models process the image with Smoothed norm algorithm. This algorithm stated that if signal or image is sufficiently sparse, we can reconstruct it from small amount of none zero basis components. The experiment is comprehensively tested under 2000 sampling images that are categorized in 18 groups by their characteristics. Moreover this sparsity number is practically used for any CS algorithm based on DCT domain.

Experimental Investigation for Practical Sparsity Number for Image Reconstruction Based on SL0 Algorithm in Discrete Frequency Domain

From the great requisitions of the modern digital image implementations, the refined spatial image is normally desired nevertheless the observed device for high resolution is ultimately overpriced thereby the SRR (Super Resolution Reconstruction) technique, which is founded on the arithmetical establishment for creating the better refined spatial image, is one of the modern scrutinized topics in DIP (Digital Image Processsing) and DSP (Digital Signal Processing) communities In consideration of ultraexpansion ratio (at 16x), this research article aims to present a modern spatial enhancing method decreed on a robust MSRR (Multiframe Super Resolution), which is constituted on the ML (Maximum Likelihood) regularization with robust Andrew's Sine function, and a SSRR (Single Frame Super Resolution), which is constituted on high-band spectrum appraisement Initially, for concealing outlier whereas preserving the region border, a sequence of captured images with poor quality is computed by a robust MSRR (Multi-Frame Super Resolution) for creating the better refined spatial image at 4x expansion ratio Next, this enlarged image is computed by high-band spectrum appraisement SSRR (Single-Frame Super Resolution) for ultimately creating the refined spatial image at 16x expansion ratio The comprehensive verification outcomes embellish that the modern spatial enhancing method can create the 16x spatial image with better refined information and less outlier

A modern spatial enhancing method decreed on a robust MSRR and high-frequency synthesized SSRR for ultra-expansion ratio

This scrutinized report presents the comprehensive scrutiny of noisy restoration technique based on HDT (Hard Decision Threshold) dissimilarity, one of the most practical and efficient dissimilarity measurement technique for localizing the impulsive noisy pixels, for RVIN (Random Value Impulsive Noise). The main contribution of this scrutinized report is the determination of the optimal window size and optimal HDT threshold of the noisy restoration technique based on HDT. From many standard tested images, the comprehensive scrutiny illustrates that the obvious difference of the HDT dissimilarity between the genuine pixel and the noisy pixel. Moreover, the statistical relationship between HDT dissimilarity and the cluster size (3x3, 5x5 or 7x7) is simulated for determine the optimized cluster size from performance perspective. Finally, the statistical relationship between the denoising performance and the Hard Decision Threshold (HDT) is simulated by using many tested images for determine the optimized threshold.

A Comprehensive Statistical Scrutiny of HDT Dissimilarity for Localizing and Restoring Impulsive Noisy Images

Due to the immense necessity of admirable electronic paintings, which must be restored from an impulsive outlier, during the last two decades, there are immense researches and developments on outlier restoration algorithms. As a results, this paper proposes the alternative impulsive noise restoration algorithm based on ROAD (Rank-Ordered Absolute Differences) and VMF (Vector Median Filter) for color electronic paintings under an impulsive noise. From theoretical point of view, ROAD is one of the superior outlier allocating and VMF is one of the superior outlier restoration for color electronic painting. By exhaustive experiment, the proposed outlier restoration algorithm is implemented on many standard electronic paintings, which are undermined by an impulsive outlier with many distribution quantities. Moreover, the state-of-art outlier restoration algorithms, for instant SMF (Standard Median Filter) and VMF (Vector Median Filter), are comparatively experimented for exposing the efficiency of the proposed algorithm.

The Impulse Outlier Suppression Techniques Using ROAD and VMF for Color Portraits

In cognitive radio, unlicensed users are allowed to opportunistically use a licensed band under the condition of harmless interference to licensed users. To ensure such condition, the key function of cognitive radio, spectrum sensing, needs to be reliable and fast. A new scheme of spectrum sensing where the sensing period is divided into multiple mini-slots is proposed to improve the performance of existing spectrum sensing techniques. Under the concept of multi mini-slots spectrum sensing the Primary User, PU, detection is performed for each mini-slot and the existence of PU is summarized by a fusion rule. In this paper, the multiple mini-slot concept is investigated and a comprehensive study is presented. The simulation results show that the more slots each sensing period is divided into, the higher the accuracy of detection becomes. The computational time is slightly increased as well, but it has no significant effect on the overall performance. The paper evaluates its performance against other conventional techniques. The results show our technique performs the best with the highest probability of detection. The computational time is kept short, similar to Energy Detection, ED.

Kornkamol Thakulsukanant

Papers

Experimental Investigation for Practical Sparsity Number for Image Reconstruction Based on SL0 Algorithm in Discrete Frequency Domain

A modern spatial enhancing method decreed on a robust MSRR and high-frequency synthesized SSRR for ultra-expansion ratio

A Comprehensive Statistical Scrutiny of HDT Dissimilarity for Localizing and Restoring Impulsive Noisy Images

The Impulse Outlier Suppression Techniques Using ROAD and VMF for Color Portraits

An Investigation into a Novel Multi Slot Spectrum Sensing for Cognitive Radio