Використання візуальних метрик для аналізу стиснення з втратами зашумлених зображень

The subject of the article is to analyze the effectiveness of lossy image compression using a BPG encoder using visual metrics as a quality criterion. The aim is to confirm the existence of an operating point for images of varying complexity for visual quality metrics. The objectives of the paper are the following: to analyze for a set of images of varying complexity, where images are distorted by additive white Gaussian noise with different variance values, build and analyze dependencies for visual image quality metrics, provide recommendations on the choice of parameters for compression in the vicinity of the operating point. The methods used are the following: methods of mathematical statistics; methods of digital image processing. The following results were obtained. Dependencies of visual quality metrics for images of various degrees of complexity affected by noise with variance equal to 64, 100, and 196. It can be seen from the constructed dependence that a working point is present for images of medium and low complexity for both the PSNR-HVS-M and MS-SSIM metrics. Recommendations are given for choosing a parameter for compression based on the obtained dependencies. Conclusions. Scientific novelty of the obtained results is the following: for a new compression method using Better Portable Graphics (BPG), research has been conducted and the existence of an operating point for visual quality metrics has been proven, previously such studies were conducted only for the PSNR metric.The test images were distorted by additive white Gaussian noise and then compressed using the methods implemented in the BPG encoder. The images were compressed with different values of the Q parameter, which made it possible to estimate the image compression quality at different values of compression ratio. The resulting data made it possible to visualize the dependence of the visual image quality metric on the Q parameter. Based on the obtained dependencies, it can be concluded that the operating point is present both for the PSNR-HVS-M metric and for the MS-SSIM for images of medium and low complexity, it is also worth noting that, especially clearly, the operating point is noticeable at large noise variance values. As a recommendation, a formula is presented for calculating the value of the compression control parameter (for the case with the BPG encoder, it is the Q parameter) for images distorted by noise with variance varying within a wide range, on the assumption that the noise variance is a priori known or estimated with high accuracy.

Low-Cost Imaging of Fluorescent DNA in Agarose Gel Electrophoresis Using Raspberry Pi Cameras

Low-cost analytical solutions built around microcomputers like the Raspberry Pi help to facilitate laboratory investigations in resource limited venues. Here, three camera modules (V1.3 with and without filter, as well as NoIR) that work with this microcomputer were assessed for their suitability in imaging fluorescent DNA following agarose gel electrophoresis. Evaluation of their utility was based on signal-to-noise (SNR) and noise variance metrics that were developed. Experiments conducted with samples were subjected to Polymerase Chain Reaction (PCR), and the amplified products were separated using gel electrophoresis and stained with Midori green. Image analysis revealed the NoIR camera performed the best with SNR and noise variance values of 21.7 and 0.222 respectively. In experiments conducted using UV LED lighting to simulate ethidium bromide (EtBr) excitation, the NoIR and V1.3 with filter removed cameras showed comparable SNR values.

A Robust Discontinuous Phase Unwrapping Based on Least-Squares Orientation Estimator

Weighted least-squares (WLS) phase unwrapping is widely used in optical engineering. However, this technique still has issues in coping with discontinuity as well as noise. In this paper, a new WLS phase unwrapping algorithm based on the least-squares orientation estimator (LSOE) is proposed to improve phase unwrapping robustness. Specifically, the proposed LSOE employs a quadratic error norm to constrain the distance between gradients and orientation vectors. The estimated orientation is then used to indicate the wrapped phase quality, which is in terms of a weight mask. The weight mask is calculated by post-processing, including a bilateral filter, STDS, and numerical relabeling. Simulation results show that the proposed method can work in a scenario in which the noise variance is 1.5. Comparisons with the four WLS phase unwrapping methods indicate that the proposed method provides the best accuracy in terms of segmentation mean error under the noisy patterns.

A Log-Exponential Shrinkage Technique for De-Noising of Ultrasound Images for Breast Cancer

Ultrasound in diagnostic imaging is well known for its safety and accessibility. But its efficiency for diagnosis is always limited by the presence of noise. So, in this study, a Log-Exponential shrinkage technique is presented for denoising of ultrasound images. A Combinational filter was designed for the removal of additive noise without losing any details. The speckle noise after homomorphic transformation follows Gaussian distribution and the conventional median estimator has very low accuracy for Gaussian distribution. The scale parameter calculated from the sub-band coefficients after homomorphic transformation was utilized to design the estimator. For shrinkage of wavelet coefficients, a multi-scale thresholding function was designed, with better flexibility. The proposed technique was tested for both medical and standard images. A significant improvement was observed in the estimation of speckle noise variance. For quantitative evaluation of the proposed technique with existing denoising methods, Mean Squared Error (MSE), Structural Similarity Index (SSIM), and Peak Signal to Noise Ratio (PSNR) were used. At the highest noise variance, the minimum improvement achieved by the proposed denoising technique in PSNR, SSIM, and MSE was 10.65%, 23.21%, and 30.46% respectively.

Practical Hybrid Beamforming for Millimeter Wave Massive MIMO Full Duplex with Limited Dynamic Range

Full-Duplex (FD) communication can revolutionize wireless communications as it doubles spectral efficiency and offers numerous other advantages over a half-duplex (HD) system. In this paper, we present a novel and practical joint hybrid beamforming (HYBF) and combining scheme for millimeter-wave (mmWave) massive MIMO FD system for weighted sum-rate (WSR) maximization with multi-antenna HD uplink and downlink users with non-ideal hardware.<br>Moreover, we present a novel interference and self-interference (SI) aware optimal power allocation scheme for the optimal beamforming directions. The analog processing stage is assumed to be quantized, and both the unit-modulus and unconstrained cases are considered.<br>Moreover, compared to the traditional sum-power constraints, the proposed algorithm is designed under the joint sum-power and the practical per-antenna power constraints. To model the non-ideal hardware of a hybrid FD transceiver, we extend the traditional limited dynamic range (LDR) noise model to mmWave. Our HYBF design relies on alternating optimization based on the minorization-maximization method. <br>We investigate the maximum achievable gain of a hybrid FD system with different levels of the LDR noise variance and with different numbers of radio-frequency (RF) chains over a HD system. Simulation results show that the mmWave massive MIMO FD systems can significantly outperform the fully digital HD systems with only a few RF chains if the LDR noise generated from the limited number of RF chains available is low. If the LDR noise variance dominates, FD communication with HYBF results to be disadvantageous than a HD system. <br>

Practical Hybrid Beamforming for Millimeter Wave Massive MIMO Full Duplex with Limited Dynamic Range

Full-Duplex (FD) communication can revolutionize wireless communications as it doubles spectral efficiency and offers numerous other advantages over a half-duplex (HD) system. In this paper, we present a novel and practical joint hybrid beamforming (HYBF) and combining scheme for millimeter-wave (mmWave) massive MIMO FD system for weighted sum-rate (WSR) maximization with multi-antenna HD uplink and downlink users with non-ideal hardware.<br>Moreover, we present a novel interference and self-interference (SI) aware optimal power allocation scheme for the optimal beamforming directions. The analog processing stage is assumed to be quantized, and both the unit-modulus and unconstrained cases are considered.<br>Moreover, compared to the traditional sum-power constraints, the proposed algorithm is designed under the joint sum-power and the practical per-antenna power constraints. To model the non-ideal hardware of a hybrid FD transceiver, we extend the traditional limited dynamic range (LDR) noise model to mmWave. Our HYBF design relies on alternating optimization based on the minorization-maximization method. <br>We investigate the maximum achievable gain of a hybrid FD system with different levels of the LDR noise variance and with different numbers of radio-frequency (RF) chains over a HD system. Simulation results show that the mmWave massive MIMO FD systems can significantly outperform the fully digital HD systems with only a few RF chains if the LDR noise generated from the limited number of RF chains available is low. If the LDR noise variance dominates, FD communication with HYBF results to be disadvantageous than a HD system. <br>

An assessment of noise variance estimations in Bayes threshold denoising under stationary wavelet domain on brain lesions and tumor MRIs

PurposeA major key success factor regarding proficient Bayes threshold denoising refers to noise variance estimation. This paper focuses on assessing different noise variance estimations in three Bayes threshold models on two different characteristic brain lesions/tumor magnetic resonance imaging (MRIs).Design/methodology/approachHere, three Bayes threshold denoising models based on different noise variance estimations under the stationary wavelet transforms (SWT) domain are mainly assessed, compared to state-of-the-art non-local means (NLMs). Each of those three models, namely D1, GB and DR models, respectively, depends on the most detail wavelet subband at the first resolution level, on the entirely global detail subbands and on the detail subband in each direction/resolution. Explicit and implicit denoising performance are consecutively assessed by threshold denoising and segmentation identification results.FindingsImplicit performance assessment points the first–second best accuracy, 0.9181 and 0.9048 Dice similarity coefficient (Dice), sequentially yielded by GB and DR; reliability is indicated by 45.66% Dice dropping of DR, compared against 53.38, 61.03 and 35.48% of D1 GB and NLMs, when increasing 0.2 to 0.9 noise level on brain lesions MRI. For brain tumor MRI under 0.2 noise level, it denotes the best accuracy of 0.9592 Dice, resulted by DR; however, 8.09% Dice dropping of DR, relative to 6.72%, 8.85 and 39.36% of D1, GB and NLMs is denoted. The lowest explicit and implicit denoising performances of NLMs are obviously pointed.Research limitations/implicationsA future improvement of denoising performance possibly refers to creating a semi-supervised denoising conjunction model. Such model utilizes the denoised MRIs, resulted by DR and D1 thresholding model as uncorrupted image version along with the noisy MRIs, representing corrupted version ones during autoencoder training phase, to reconstruct the original clean image.Practical implicationsThis paper should be of interest to readers in the areas of technologies of computing and information science, including data science and applications, computational health informatics, especially applied as a decision support tool for medical image processing.Originality/valueIn most cases, DR and D1 provide the first–second best implicit performances in terms of accuracy and reliability on both simulated, low-detail small-size region-of-interest (ROI) brain lesions and realistic, high-detail large-size ROI brain tumor MRIs.

Robustness and Sensitivity Tuning of the Kalman Filter for Speech Enhancement

Inaccurate estimates of the linear prediction coefficient (LPC) and noise variance introduce bias in Kalman filter (KF) gain and degrade speech enhancement performance. The existing methods propose a tuning of the biased Kalman gain, particularly in stationary noise conditions. This paper introduces a tuning of the KF gain for speech enhancement in real-life noise conditions. First, we estimate noise from each noisy speech frame using a speech presence probability (SPP) method to compute the noise variance. Then, we construct a whitening filter (with its coefficients computed from the estimated noise) to pre-whiten each noisy speech frame prior to computing the speech LPC parameters. We then construct the KF with the estimated parameters, where the robustness metric offsets the bias in KF gain during speech absence of noisy speech to that of the sensitivity metric during speech presence to achieve better noise reduction. The noise variance and the speech model parameters are adopted as a speech activity detector. The reduced-biased Kalman gain enables the KF to minimize the noise effect significantly, yielding the enhanced speech. Objective and subjective scores on the NOIZEUS corpus demonstrate that the enhanced speech produced by the proposed method exhibits higher quality and intelligibility than some benchmark methods.

Automated Pre-Processing and Automated Post-Processing in EEG/MEG Brain Source Analysis

Analyzing Electroencephalography (EEG)/Magnetoencephalography (MEG) brain source signals allows for a better understanding and diagnosis of various brain-related activities or injuries. Due to the high complexity of the mentioned measurements and their low spatial resolution, different techniques have been employed to enhance the quality of the obtained results. The objective of this work is to employ state-of-the-art approaches and develop algorithms with higher analysis reliability. As a pre-processing method, subspace denoising and artifact removal approaches are taken into consideration, to provide a method that automates and improves the estimation of the Number of Component (NoC) for artifacts such as Eye Blinking (EB). By using synthetic EEG-like simulation and real MEG data, it is shown that the proposed method is more reliable over the conventional manual method in estimating the NoC. For Independent Component Analysis (ICA)-based approaches, the proposed method in this thesis provides an estimation for the number of components with an accuracy of 98.7%. The thesis is also devoted to improving source localization techniques, which aims to estimate the location of the source within the brain, which elicit time-series measurements. In this context, after obtaining a practical insight into the performance of the popular L2-Regularization based approaches, a post-processing thresholding method is introduced. The proposed method improves the spatial resolution of the L2-Regularization inverse solutions, especially for Standard Low-Resolution Electromagnetic Tomography (sLORETA), which is a well-known and widely used inverse solution. As a part of the proposed method, a novel noise variance estimation is introduced, which combines the kurtosis statistical parameter and data (noise) entropy. This new noise variance estimation technique allows for a superior performance of the proposed method compared to the existing ones. The algorithm is validated on the synthetic EEG data using well-established validation metrics. It is shown that the proposed solution improves the resolution of conventional methods in the process of thresholding/denoising automatically and without loss of any critical information.