Post-processing Method Research Articles

Facial expression morphing: enhancing visual fidelity and preserving facial details in CycleGAN-based expression synthesis

Recent advancements in facial expression synthesis using deep learning, particularly with Cycle-Consistent Adversarial Networks (CycleGAN), have led to impressive results. However, a critical challenge persists: the generated expressions often lack the sharpness and fine details of the original face, such as freckles, moles, or birthmarks. To address this issue, we introduce the Facial Expression Morphing (FEM) algorithm, a novel post-processing method designed to enhance the visual fidelity of CycleGAN-based outputs. The FEM method blends the input image with the generated expression, prioritizing the preservation of crucial facial details. We experimented with our method on the Radboud Faces Database (RafD) and evaluated employing the Fréchet Inception Distance (FID) standard benchmark for image-to-image translation and introducing a new metric, FSD (Facial Similarity Distance), to specifically measure the similarity between translated and real images. Our comprehensive analysis of CycleGAN, UNet Vision Transformer cycle-consistent GAN versions 1 (UVCGANv1) and 2 (UVCGANv2) reveals a substantial enhancement in image clarity and preservation of intricate details. The average FID score of 31.92 achieved by our models represents a remarkable 50% reduction compared to the previous state-of-the-art model’s score of 63.82, showcasing the significant advancements made in this domain. This substantial enhancement in image quality is further supported by our proposed FSD metric, which shows a closer resemblance between FEM-processed images and the original faces.

Strong Robust LIO System Based on Event Camera Assistance

Abstract. At present, the LiDAR system can achieve high precision and is extensively used for indoor and outdoor mobile positioning and mapping. However, LiDAR systems still face issues in cluttered environments where strong features are absent, leading to a degradation of the LiDAR-based solution. When the carrier movement involves high-speed or prolonged exposure to the mirror wall, it can cause severe degradation issues or even positioning failures in the laser slam system. Event cameras are vision sensors inspired by biology that exhibit strong robustness in high dynamic and low texture environments, potentially leading to better performance in such environments. However, some issues with event cameras remain unresolved. In this paper, a multi-source fusion method based on EVIO, LIO and IMU trajectory layer post-processing method is proposed, which will fully consider the robustness of event camera in high dynamic environment and the high precision advantage of Lidar in conventional environment, and use an algorithm based on normalized uncertainty. The elastic multi-source fusion of event camera and LiDAR is realized and tested in realde environment. Experimental results show that the proposed algorithm can effectively improve the accuracy of event camera and LiDAR. Compared to the current more advanced algorithms, the proposed algorithm effectively addresses the problem of LIO in degraded environments. Additionally, it mitigates the scale inaccuracy and divergence of the EVIO trajectory to some extent. Compared to LIO, the algorithm can reduce the maximum position error by approximately 30% and increase the overall position accuracy by 32%. Additionally, it can significantly constrain the divergence of errors in the Y direction, improving its accuracy by about 75% and 65% compared to the LIO and EVIO algorithms, respectively.

Improving microvascular sensitivity of color Doppler using phase mask based flow recycling algorithm


Blood flow sensitivity is a crucial metric for appraising the effectiveness of color Doppler flow imaging (CDFI). Color Doppler velocity maps based on classic autocorrelation techniques are widely used in clinical practice. However, these techniques often produce twinkling artifacts in noisy regions due to the inherent randomness of noise phases. To mitigate artifacts and improve image quality, Power Mask (PoM) technology becomes imperative. Nevertheless, PoM technology unintentionally filters out small flow signals that have similar power and frequency characteristics to noise signals, thereby reducing the imaging system's sensitivity to flow. 
Approach:
To address this issue, a novel Flow Recycling Algorithm (FRA) based on phase anomaly is introduced in this study. This algorithm, excavating small flow signals from noise, aims to enhance the small flow signals with low-velocity by the phase characteristics of the color Doppler flow information. 
Main results: 
Experiments in multi-organ imaging have shown that the FRA-CDFI approach is more effective in suppressing twinkling artifacts in noisy regions, preserving intricate small flow signals, and markedly improving small blood flow sensitivity. This novel approach provides adequate technical support for clinical ultrasound imaging of organs with dense small blood vessels, such as the brain, kidneys, liver, and more. 
Significance: 
As a novel post-processing method, FRA-CDFI holds significant potential for future deployment in clinical high-frame-rate ultrasound imaging devices.

Picometer-Sensitivity Surface Profile Measurement Using Swept-Source Phase Microscopy

In recent years, the Swept-Source Phase Microscope (SS-PM) has gained more attention due to its greater robustness to sample motion and lower signal decay with depth. However, the mechanical wavelength tuning of the swept source creates small variations in the wavenumber sampling of spectra that introduce serious phase noise. We present a software post-processing method to eliminate phase noise in SS-PM. This method does not require high-quality swept light sources or high-precision synchronization devices and achieves ~72 pm displacement sensitivity using a conventional SS-PM system. We compare the performance of this method with traditional software-based methods by measuring phase fluctuations. The phase fluctuations in the traditional software-based method are five times those of the proposed method, which means the proposed method has better sensitivity. Using this method, we reconstructed phase images of air wedges and resolution plates to demonstrate the SS-PM’s potential for high-sensitivity surface profiling measurement. Finally, we discuss the advantages of SS-PM over traditional Spectral-Domain PM techniques.

Detecting chord tone alterations and suspensions

Chord detection is the task of assigning chord labels to segments of an input musical piece. The labels are typically drawn from a pre-defined vocabulary in which each symbol comprises at least a root pitch and a chord quality. In this paper, we introduce a post-processing method that takes as input a musical score with such labels and adds chord tone alterations such as suspensions to them. Although it is an important aspect of harmonic analysis, it is infeasible to do so by simply adding features to the labels in the vocabulary, given the multiplicative effect on vocabulary sizes which can already be over 1000. We evaluate our method using both ground truth input and input derived from a chord detection model, showing that it outperforms a strong heuristic baseline in both cases. Furthermore, we show that our model can be used to detect the rarer chord qualities from the model's output alterations, potentially enabling chord detection models to be trained on small vocabularies containing only the more common (and easier to identify) chord qualities (e.g. only triads).

Generalisation of the yield stress measurement in three point bending collapse tests: application to 3D printed flax fibre reinforced hydrogels

This paper describes the extrusion pressure's effect on composite hydrogel inks' filaments subjected to three point bending collapse tests. The composite considered in this work consists of an alginate-poloxamer hydrogel reinforced with flax fibres. Increased extrusion pressure resulted in more asymmetrical filaments between the support pillars. Furthermore, the material and printing conditions used in the present study led to the production of curved specimens. These two characteristics implicitly limit the validity of the yield stress equations commonly used in open literature. Therefore, a new system of equations was derived for the case of asymmetrical and curved filaments. A post-processing method was also created to obtain the properties required to evaluate this yield stress. This new equation was then implemented to identify the strength of failed hydrogels without flax fibre reinforcement. A statistical analysis showed this new equation's significance, which yielded statistically higher (i.e. 1.15 times larger) strength values compared to the numbers obtained with the open literature equations. At larger extrusion pressures, longer periods were needed for the material to converge towards its final shape. Larger extrusion pressure values led to lower yield stresses within the composite hydrogel filament: a 5 kPa increase in extrusion pressure lowered the yield stress by 19%. In comparison, a 15 kPa increase led to a 29% decrease in the yield stress. Overall this study provides guidelines to standardize three point bending collapse tests and analysis comparison between different materials.

Noise-robust and data-efficient compressed ghost imaging via the preconditioned S-matrix method

The design of the illumination pattern is crucial for improving imaging quality of ghost imaging (GI). The S-matrix is an ideal binary matrix for use in GI with non-visible light and other particles since there are no uniformly configurable beam-shaping modulators in these GI regimes. However, unlike widely researched GI with visible light, there is relatively little research on the sampling rate and noise resistance of compressed GI based on the S-matrix. In this paper, we investigate the performance of compressed GI using the S-matrix as the illumination pattern (SCSGI) and propose a post-processing method called preconditioned S-matrix compressed GI (PSCSGI) to improve the imaging quality and data efficiency of SCSGI. Simulation and experimental results demonstrate that compared with SCSGI, PSCSGI can improve imaging quality in noisy conditions while utilizing only half the amount of data used in SCSGI. Furthermore, better reconstructed results can be obtained even when the sampling rate is as low as 5%. The proposed PSCSGI method is expected to advance the application of binary masks based on the S-matrix in GI.

Autonomous Underwater Vehicle Navigation Enhancement by Optimized Side-Scan Sonar Registration and Improved Post-Processing Model Based on Factor Graph Optimization

Autonomous Underwater Vehicles (AUVs) equipped with Side-Scan Sonar (SSS) play a critical role in seabed mapping, where precise navigation data are essential for mosaicking sonar images to delineate the seafloor’s topography and feature locations. However, the accuracy of AUV navigation, based on Strapdown Inertial Navigation System (SINS)/Doppler Velocity Log (DVL) systems, tends to degrade over long-term mapping, which compromises the quality of sonar image mosaics. This study addresses the challenge by introducing a post-processing navigation method for AUV SSS surveys, utilizing Factor Graph Optimization (FGO). Specifically, the method utilizes an improved Fourier-based image registration algorithm to generate more robust relative position measurements. Then, through the integration of these measurements with data from SINS, DVL, and surface Global Navigation Satellite System (GNSS) within the FGO framework, the approach notably enhances the accuracy of the complete trajectory for AUV missions. Finally, the proposed method has been validated through both the simulation and AUV marine experiments.

Real-Time Prediction of Resident ADL Using Edge-Based Time-Series Ambient Sound Recognition.

To create an effective Ambient Assisted Living (AAL) system that supports the daily activities of patients or the elderly, it is crucial to accurately detect and differentiate user actions to determine the necessary assistance. Traditional intrusive methods, such as wearable or object-attached devices, can interfere with the natural behavior of patients and may lead to resistance. Furthermore, non-intrusive systems that rely on video or sound data processed by servers or the cloud can generate excessive data traffic and raise concerns about the security of personal information. In this study, we developed an edge-based real-time system for detecting Activities of Daily Living (ADL) using ambient noise. Additionally, we introduced an online post-processing method to enhance classification performance and extract activity events from noisy sound in resource-constrained environments. The system, tested with data collected in a living space, achieved high accuracy in classifying ADL-related behaviors in continuous events and successfully generated user activity logs from time-series sound data, enabling further analyses such as ADL assessments. Future work will focus on enhancing detection accuracy and expanding the range of detectable behaviors by integrating the activity logs generated in this study with additional data sources beyond sound.

An improved infrared image post-processing method for metals and composites

Post-processing of the infrared thermography image is crucial for nondestructive testing, which will reduce noise and disturbance considerably. In this work, an improved image post-processing method based on the temperature ratio is proposed, namely the Thermal Image Division (TID) method. To validate this method, we carried out experiments on different materials such as composite CFRP (carbon fiber reinforced polymer) plates, 304 stainless steel plates and aluminum alloy samples. The results show the proposed method is pretty useful, significantly improves the signal-to-noise ratio of the image by comparing with other post-processing methods. It’s not only beneficial for identifying the edge of the defect, but also facilitates the detection of smaller defects with deeper depth. The signal-to-noise ratio is improved by nearly 117.5 % from practice. The TID method was applied to the defect size detection of pulse thermography and ultrasonic thermography. The results indicate TID method has good robustness, significantly reduces the error of defect size quantification.

Polymer microsphere inks for semi-solid extrusion 3D printing at ambient conditions

Extrusion-based 3D printing methods have great potential for manufacturing of personalized polymer-based drug-releasing systems. However, traditional melt-based extrusion techniques are often unsuitable for processing thermally labile molecules. Consequently, methods that utilize the extrusion of semi-solid inks under mild conditions are frequently employed. The rheological properties of the semi-solid inks have a substantial impact on the 3D printability, making it necessary to evaluate and tailor these properties. Here, we report a novel semi-solid extrusion 3D printing method based on utilization of a Carbopol gel matrix containing various concentrations of polymeric microspheres. We also demonstrate the use of a solvent vapor-based post-processing method for enhancing the mechanical strength of the printed objects. As our approach enables room-temperature processing of polymers typically used in the pharmaceutical industry, it may also facilitate the broader application of 3D printing and microsphere technologies in preparation of personalized medicine.

Автоматизований метод перевірки та налагодження тестових сценаріїв на основі формальної моделі

Introduction. Model-based test cases generation is a popular strategy for test automation. It helps to reduce time spent on the development of a test suite and can improve level of coverage. However, many reports show shortage of such test cases in poor quality and doubtable efficiency. Purpose. The main goal of the proposed method is cost-effective validation, assessment, debugging and concretization of generated test cases. The method helps improve quality and efficiency of the test cases, make their scenario meaningful and goal-oriented. The method also develops debugging facilities and simplifies data dependency analysis and test scenario editing. Methods. We propose an automated post-processing method which allows to evaluate path that is examined by the test case, and to make safe changes to the path which will eliminate the shortcomings while leaving the coverage targets of the test case unharmed. The method is based on visualization of the path along the control flow graph of the model with additional information about factual evaluation history of all variables and possible alternative variants of behavior. For consistent substitution of certain values in the signal parameters, which would determine the artifacts of the test environment (such as, for example, files, databases, etc.) and check boundary cases (in predicates of conditions, indexing of arrays, etc.), a method of interactive specification of symbolic traces has been developed. Results. The role of the user in deciding whether to add a test case to the project test suite and make changes to it remains crucial, but to reduce labor intensity, the following processes are automated: evaluation of test scenarios according to certain objective characteristics (level of coverage, ability to detect defects, data cohesion, etc.); highlighting of possible alternatives for making corrections; consistent updating of computations for the corresponding corrections. A prototype was developed based on the proposed methods. The empirical results demonstrated a positive impact on the overall efficiency (ability to detect defects and reduce resource consumption) and quality (meaningfulness, readability, maintenance, usefulness for debugging, etc) of the generated test suites. The method allows to make automatically generated test cases trustable and usable. Conclusion. The proposed toolkit significantly reduces the time spent on researching the results of test generation and validation of the obtained tests and their editing. Unlike existing simulation methods, the proposed method not only informs about the values of variables, but also explores the history of their computations and additionally provides information about admissible alternatives. Further we plan to improve the process of localizing the causes of test failure at execution phase to speed up the search for defects.

A Bibliometric Analysis of the Global Trend of Residual Stress Induced by WAAM Process and Stress Relief Heat Treatment

Gathering data from previous research and incorporating it into a scientometrics investigation can provide valuable insights into a particular area of study. Wire Arc Additive Manufacturing (WAAM) is currently popular for producing customized metal products quickly. Despite this, there have been lack of studies on implementing stress relief heat treatment after the manufacturing process to reduce residual stress in the resulting parts potentially. This paper represents the first bibliometric analysis study focusing on the application of stress-relief heat treatment as a post-processing method for wire arc additive manufacturing (WAAM). Given the exploratory nature of this research, it is expected that future investigations will continue to delve into these areas, ultimately contributing to a better understanding of the effects of stress-relief heat treatment on the additively manufactured materials.

Construction of Vine-Inspired Antimicrobial Filter with Multiscale 3D Nanonetwork for High-Efficiency Air Filtration.

Enhancing the antimicrobial activity of high-efficiency particulate air (HEPA) filters while maintaining filtration efficiency and pressure drop is currently an urgent issue for preventing the spread of pathogenic microorganisms. Herein, inspired by vines which can enwind fences to fix as well as decorate them, a flexible antimicrobial chitin nanofiber (ChNF@CuOx) was fabricated and loaded onto the rigid glass fiber (GF) skeleton of a HEPA filter. Through the physical interaction, ChNF@CuOx was spontaneously enwound on GF, and ChNF@CuOx itself interweaved to form a new nanonetwork between the GF skeleton. The obtained antimicrobial air filter (ChNF@CuOx/GF) with a unique nanonetwork increased the filtration efficiency of the HEPA filter. Meanwhile, it possessed excellent inactivation ability against Staphylococcus aureus, Escherichia coli, and Candida albicans due to the urchin-like in situ grown CuOx on the ChNF. In particular, the oxygen vacancies generated unexpectedly in CuOx enabled it to produce reactive oxygen species. After eight cycles of antimicrobial assays, the antimicrobial rates of bacteria were higher than 99.5%, and those of fungi were greater than 98.3%. The successful synthesis of antimicrobial fibers and the construction of multidimensional nanoscale structures through a simple postprocessing method provide a new design mentality for antimicrobial functionalization for HEPA filters.

Super high strength of annealed Fe32Cr33Ni29Al3Ti3 manufactured by selective laser melting

Selective laser melting (SLM) of high entropy alloys (HEAs) shows great potential in industry applications and annealing was always used as a post-processing method after SLM. A dual-phase (DP) Co-free HEA was manufactured by SLM and annealed at 900 ℃ in this work. The annealed HEA exhibited super high yield and tensile strengths (∼1224 and ∼1363MPa, respectively), higher than those of the as-printed sample, primarily attributed to grain refinement and spinodal decomposition with FCC phase (rich in Ni/Al/Ti) and BCC phase (rich in Cr/Fe) decomposing into FCC (rich in Fe), B2 (rich in Cr, poor in Ni), and L12 (rich in Ni/Al/Ti, poor in Fe/Cr) during annealing. The annealed DP Co-free HEA exhibits notably superior strength to other Co-free HEAs or medium entropy alloys (MEAs) produced via SLM. Due to their affordability, Co-free HEAs show immense potential for widespread commercial use, and this study explores annealing in SLM to improve the mechanical properties of HEAs. The high-strength annealed DP Co-free HEA could be especially valuable for structural elements in construction applications.

Research on ultrasonic phased array total focusing multi-mode fusion method

Abstract Welded seams are one of the important structural forms in the construction of industrial equipment. But during use, hazardous defects may occur within the weld. Ultrasonic phased-array total focusing method (TFM) is a relatively new post-processing imaging method that can locate defects. In this study, a multi-mode ultrasonic phased-array total focusing fusion method was explored for weld defects to achieve effective quantification and localization of different types of defects. This study explores the application of different fusion methods such as maximum fusion, average fusion and effective region fusion in multi-mode phased array total focusing method. The experimental results show that the fusion methods can improve the accuracy of defect detection compared to single imaging methods, but the effective area fusion method has the best results in detecting weld defects. Taken together, the effective region fusion method can effectively improve the resolution and signal-to-noise ratio of multi-mode imaging methods.

Comparison of acoustic, optical, and heat release rate based flame transfer functions for a lean-burn injector under engine-like conditions

Determining the flame transfer function plays a crucial role during the development phase of lean-burn injectors to predict the overall stability of the combustion system. This study develops an enhanced acoustic post-processing strategy using acoustic network modeling and compressible large-eddy simulation for a lean-burn aero-engine injector in a realistic test rig. Results show that optical flame transfer functions experimentally obtained align with those derived from large-eddy simulation based on heat release rate. However, discrepancies, especially in gain, are observed when using the standard acoustic post-processing method. By employing an acoustic network model, it is shown that comparable flame transfer functions can be achieved through refined post-processing strategies. This study highlights the limitations of conventional acoustic post-processing and underscores the necessity of explicit acoustic modeling in complex setups to accurately obtain the flame response. A generalized formulation applicable to a broader range of setups, extending beyond simple configurations, is presented as an alternative to the conventional acoustic post-processing method.

A post-processing method called Fourier transform based on local maxima of autocorrelation function for extracting fault feature of bearings

Accurate extraction of fault features from signals containing weak repetitive pulses is a critical issue in fault diagnosis of bearings. To address this challenge, a novel solution termed Fourier transform based on Local Maxima (FT-LM) is proposed in this paper. In the FT-LM method, local maxima are first extracted from signals, followed by the application of a Fourier transform to this sequence. Furthermore, to enhance the detection of repetitive pulses, the Short-Time Fourier Transform (STFT) and the unbiased autocorrelation function are integrated into the FT-LM, developing a post-processing algorithm called Fourier Transform based on Local Maxima of Autocorrelation Function (FT-LMACF). By employing a series of simulated signals and two sets of public experimental data, comparisons are made among the FT-LMACF, mainstream post-processing methods, the Kurtogram method and the spectral coherence based on the STFT. The results indicate that the FT-LMACF outperforms other algorithms in extracting fault features inherent in repetitive pulses.

Application of weather post-processing methods for operational ensemble hydrological forecasting on multiple catchments in Canada

Hydrological forecasts contain biases that need to be addressed for their effective use in operational decision-making in water resources management. Performing post-processing allows reducing the overall systematic bias while improving the distribution and accuracy of hydrological forecasts. In this study, a Quantile Mapping (QM) post-processing method was applied on weather forecasts following three temporal configurations (monthly, seasonal, and annual) of the quantile mapping scheme. The evaluation encompasses 20 catchments in southern Canada, employing a leave-one-out approach with the QM method on ECMWF ensemble weather forecasts spanning 2015–2020 inclusively. These processed forecasts are subsequently utilized as forcings for eight hydrological models, generating ensemble streamflow forecasts over a 6-year period with a lead time of 10 days and a sub-daily timestep of 6 h. The performance of the QM method is mainly assessed using the Continuous Ranked Probability Score (CRPS) metric, in complement with a forecast reliability score (ABDU) and a forecast sharpness metric (NMIQR). Significant improvements are discerned in precipitation forecasts upon the application of QM. Notably, these improvements are translated into enhanced hydrological forecasts for over half of the catchments studied (55 %). Surprisingly, no discernible differences in performance are observed among the three QM configurations in most catchments. Interestingly, there are watersheds where the implementation of QM exhibit either poorer or no change in performance and sharpness compared to raw forecasts.

Horizontal rearrangement frequency domain chirplet transform: algorithm and applications

Abstract Based on the idea of optimal time–frequency analysis (TFA) theory, a time–frequency post-processing method called horizontal rearrangement frequency domain chirplet transform (HRFCT) is proposed. The proposed method is designed to analyze non-stationary transient signals with nonlinear group delay (GD), aiming at obtaining energy-concentrated and accurate time–frequency representation (TFR). First, based on the image characteristics of the constructed amplitude function, the selection criteria of GD are defined, that is, the local maximum of amplitude function and the local minimum of absolute value of derivative of amplitude function are calculated. Next, according to the calculated GD, the coefficients on the GD trajectory are adaptively extracted, and the coefficients that cause the TFR to be blurred are discarded. Finally, the analysis result of a two-component simulation signal demonstrates the satisfactory performance of the HRFCT, and compared with four advanced TFA methods, the competitiveness of the HRFCT is verified. Additionally, the application of the HRFCT in the processing of constant and variable speed bearing signals highlights its prospects in transient signal analysis and rotating machinery condition monitoring.