Image Processing Perspective Research Articles

IDNA-ITLM: An interpretable and transferable learning model for identifying DNA methylation.

In this study, from the perspective of image processing, we propose the iDNA-ITLM model, using a novel data enhance strategy by continuously self-replicating a short DNA sequence into a longer DNA sequence and then embedding it into a high-dimensional matrix to enlarge the receptive field, for identifying DNA methylation sites. Our model consistently outperforms the current state-of-the-art sequence-based DNA methylation site recognition methods when evaluated on 17 benchmark datasets that cover multiple species and include three DNA methylation modifications (4mC, 5hmC, and 6mA). The experimental results demonstrate the robustness and superior performance of our model across these datasets. In addition, our model can transfer learning to RNA methylation sequences and produce good results without modifying the hyperparameters in the model. The proposed iDNA-ITLM model can be considered a universal predictor across DNA and RNA methylation species.

Addressing preprocessing for spectrum sensing using image processing

This paper presents a novel approach to preprocessing spectrograms for spectrum sensing (SS) from the image-processing perspective. Gaussian bilateral filtering, a common image-denoising technique, has been introduced to improve spectrograms in SS in high-noise environments. This approach is evaluated by simulating LTE and 5 G NR signal spectrograms across various signal-to-noise ratios (SNRs). An extensive review and comparison with recent spectrogram-based works for different applications demonstrated that the proposed approach does not depend on deep learning models to denoise spectrograms, showing a simpler yet effective strategy to address SS.

Federated recurrent-based adaptive battle royale algorithm for privacy-preserving data aggregation in industrial IoT: a signal, image, and video processing perspective

Federated recurrent-based adaptive battle royale algorithm for privacy-preserving data aggregation in industrial IoT: a signal, image, and video processing perspective

Generalized load graphical forecasting method based on modal decomposition

In a “low-carbon” context, the power load is affected by the coupling of multiple factors, which gradually evolves from the traditional “pure load” to the generalized load with the dual characteristics of “load + power supply.” Traditional time-series forecasting methods are no longer suitable owing to the complexity and uncertainty associated with generalized loads. From the perspective of image processing, this study proposes a graphical short-term prediction method for generalized loads based on modal decomposition. First, the datasets are normalized and feature-filtered by comparing the results of Xtreme gradient boosting, gradient boosted decision tree, and random forest algorithms. Subsequently, the generalized load data are decomposed into three sets of modalities by modal decomposition, and red, green, and blue (RGB) images are generated using them as the pixel values of the R, G, and B channels. The generated images are diversified, and an optimized DenseNet neural network was used for training and prediction. Finally, the base load, wind power, and photovoltaic power generation data are selected, and the characteristic curves of the generalized load scenarios under different permeabilities of wind power and photovoltaic power generation are obtained using the density-based spatial clustering of applications with noise algorithm. Based on the proposed graphical forecasting method, the feasibility of the generalized load graphical forecasting method is verified by comparing it with the traditional time-series forecasting method.

An objective quality indicator for evaluating filtering of microscopic video in CASA system

Computer-Aided Sperm Analysis (CASA) has received great attention in the diagnosis of male reproductive health for its objectivity and accuracy. But its accuracy may be greatly affected by algorithms used in microscopic video preprocessing, especially algorithms for microscopic videos filtering. Owing to the Peak Signal to Noise Ratio (PSNR) and Structural Similarity (SSIM) indicators are not suitable for microscopic images, objective indicators for evaluating filtering of microscopic videos are scarce at present, leading to filtering algorithms are chosen arbitrarily. In addition, it is critical to establish quality control indicators for CASA from the perspective of image processing, but they are rarely mentioned for the reason that algorithms adopted in image processing aren’t public. To reduce influences of filtering on diagnostic results, it is imperative to establish an objective quality control indicator for evaluating filtering of microscopic video from the perspective of image preprocessing. The principle of formatting sperm tracking trajectory and the rules of grading trajectory continuity were introduced firstly. Then, impacts of filtering algorithms on the continuity of sperm trajectory were investigated and the relation between the continuity of sperm trajectory and the fluctuation of sperm segmentation thresholds was further explored. Finally, an objective indicator for appraising the quality of filtering for microscopic video was established based on SADTM (Sum of Absolute Difference between Threshold and the Mean).The proposed indicator can objectively evaluate the quality of microscopic video filtering and provide a guideline for algorithm selection, laying foundation for quality control for CASA.

Objective Prediction of Human Visual Acuity Using Image Quality Metrics

This work addresses the objective prediction of human uncorrected decimal visual acuity, an unsolved challenge due to the contribution of both physical and neural factors. An alternative approach to assess the image quality of the human visual system can be addressed from the image and video processing perspective. Human tolerance to image degradation is quantified by mean opinion scores, and several image quality assessment algorithms are used to maintain, control, and improve the quality of processed images. The aberration map of the eye is used to obtain the degraded theoretical image from a set of natural images. The amount of distortion added by the eye to the natural image was quantified using different image processing metrics, and the correlation between the result of each metric and subjective visual acuity was assessed. The correlation obtained for a model based on a linear combination of the normalized mean square error metric and the feature similarity index metric was very good. It was concluded that the proposed method could be an objective way to determine subjects’ monocular and uncorrected decimal visual acuity with low uncertainty.

Research on speech style transfer algorithm combined with image processing perspective

Speech, as the acoustic expression of language, is one of the most natural and effective means of human information communication. With the rapid development of the Internet and communication technology, the function of robot voice interaction is more and more popular among people. However, the robotic pronunciation cannot meet people's demand for personalized voice interaction. At the same time, style transfer technology, which is widely used in image and video processing, has been relatively mature. By studying the theoretical methods of generalized style transfer technology (including the style transfer of images and video signals), and comparing and analyzing various machine learning algorithms used by the current voice style transfer technology, this paper draws the following conclusions: First, various models generally have the problem of large demand for training data and difficulty in training. Second, an algorithm model shows the alienation effect in different usage scenarios. Finally, based on the above problems, suggestions for the development of voice style transfer are put forward.

Dual-kernel driven convolutional sparse learning for bearing transient feature enhancement

The transient structure feature of bearing reflects the dynamic response of vibration, which is of vital importance for rotating machinery fault diagnosis. However, the measured signals are complicated and non-stationary, where the transient impacts are always submerged in heavy background noise. In this study, a novel dual-kernel driven convolutional sparse learning (DKCSL) is proposed to enhance the transient feature with the intrinsic structure mined. Considering the benefits of data-driven representation with time–frequency manifold (TFM) learning and model-driven representation with wavelet analysis, two time–frequency kernels with different principles are constructed for 2-dimensional convolutional sparse learning (CSL) in a fusion way of both data-driven and model-driven. From the perspective of image processing, the representation of desired transient structure features can be well sensed from time-frequency distribution (TFD) by those two dual-driven time–frequency kernels. In this manner, the desired defective intrinsic features can be mined, which is consistent with the physical dynamic response. In addition, image entropy is introduced to output the optimal TFM kernel and a series of inverse transform are applied to obtain the enhanced signal waveform. Simulated and experimental data are tested here to verify the creativity and effectiveness of the proposed DKCSL method, and a comparison with the traditional single TFM kernel-based sparse result and wavelet kernel-based sparse result are further provided, which deeply indicates the originality of our method that its potential in intrinsic structure feature extraction with dual-kernel sensing for bearing fault diagnosis.

An Add-on CNN based Model for the Detection of Tuberculosis using Chest X-ray Images

Machine Learning has been potentially contributing towards smart diagnosis in the medical domain for more than a decade with a target towards achieving higher accuracy in detection and classification. However, from the perspective of medical image processing, the contribution of machine learning towards segmentation is not been much to find in recent times. The proposed study considers a use case of Tuberculosis detection and classification from chest x-rays where a unique machine learning approach of Convolution Neural Network is adopted for segmentation of lung images from CXR. A computational framework is developed that performs segmentation, feature extraction, detection, and classification. The proposed system's study outcome is analyzed with and without segmentation over existing machine learning models to exhibit 99.85% accuracy, which is the highest score to date in contrast to existing approaches found in the literature. The study outcome based on the comparative analysis exhibits the effectiveness of the proposed system.

Wavelet based tone mapping (TM) enhancement to a detection system for faint and compact sources in HDR and large FOV radio scenes

For high dynamic range (HDR) and large field-of-view (FOV) radio scenes, detection of faint sources with compact support and tiny sizes in comparatively extensive celestial areas is a challenging task. In the era of the Square Kilometer Array (SKA), it will be of particular concern, and data-targeted systematic methods are put on the agenda. This paper proposes a systematic method to solve this problem. From the perspective of image processing, a multiscale image contrast enhancement (ICE) is proposed to normalize the HDR. Source representation is realized by a starlet framework, where a two-step tone mapping (TM) including an arc-tangent function compression and an aggregated gain function enhancement are embedded. According to two bionic principles, the gain function is designed to highlight faint sources, remove artifacts (e.g., halos), and avoid feature distortions. For source detection, an integration method is proposed for projections between the celestial and the planar coordinate systems. It serves for coordinate transform and source location. A detection indicator system is built, and elaborate discrimination principles are drawn up. In the experiments, this method is dedicated to the dataset of the TIFR GMRT Sky Survey (TGSS) alternative data release 1 (ADR1) to facilitate the scientific goal of discovering new sources. Results show its advantages over the state of the art methods in visual inspections and detection ratios, and preliminarily prove that it has prospects for future HDR and large FOV radio data.

Cloud Contaminated Multispectral Remote Sensing Image Enhancement Algorithm Based on MobileNet

Multispectral remote sensing images have shown unique advantages in many fields, including military and civilian use. Facing the difficulty in processing cloud contaminated remote sensing images, this paper proposes a multispectral remote sensing image enhancement algorithm. A model is constructed from the aspects of cloud detection and image enhancement. In the cloud detection stage, clouds are divided into thick clouds and thin clouds according to the cloud transmitability in multi-spectral images, and a multi-layer cloud detection model is established. From the perspective of traditional image processing, a bimodal pre-detection algorithm is constructed to achieve thick cloud extraction. From the perspective of deep learning, the MobileNet algorithm structure is improved to achieve thin cloud extraction. Faced with the problem of insufficient training samples, a self-supervised network is constructed to achieve training, so as to meet the requirements of high precision and high efficiency cloud detection under the condition of small samples. In the image enhancement stage, the area where the ground objects are located is determined first. Then, from the perspective of compressed sensing, the signal is analyzed from the perspective of time and frequency domains. Specifically, the inter-frame information of hyperspectral images is analyzed to construct a sparse representation model based on the principle of compressed sensing. Finally, image enhancement is achieved. The experimental comparison between our algorithm and other algorithms shows that the average Area Overlap Measure (AOM) of the proposed algorithm reaches 0.83 and the Average Gradient (AG) of the proposed algorithm reaches 12.7, which is better than the other seven algorithms by average AG 2.

Pulsar Candidate Recognition Using Deep Neural Network Model

With an improvement in the performance of radio telescopes, the number of pulsar candidates has increased rapidly, which makes selecting valuable pulsar signals from the candidates challenging. It is imperative to improve the recognition efficiency of pulsars. Therefore, we solved this problem from the perspective of intelligent image processing and a deep neural network model AR_Net was proposed in this paper. A single time–phase-subgraph or frequency-phase-subgraph was used as the judgment basis in the recognition model. The convolution blocks can be obtained by combining the attention mechanism module, feature extractor and residual connection. Then, different convolution blocks were superimposed to constitute the AR_Net to screen pulsars. The attention mechanism module was used to calculate the weight through an additional feedforward neural network and the important features in the sample were identified by weight, so the ability of the model to learn pivotal information was improved. The feature extractor was used to gain the high-dimensional features in the samples and the residual connection was introduced to alleviate the problem of network degradation and intensify feature reuse. The experimental results show that AR_Net has higher F1-score, recall and accuracy, and our method produces a competitive result compared with previous methods.

A Deep Learning-Based Piano Music Notation Recognition Method.

In the era of rapid development of computer technology, piano music notation and electronic synthesis system can be established using computer technology, and the basic laws of music score can be analyzed from the perspective of image processing, which is of a great significance in promoting piano improvement and research and development, etc. In this paper, the Beaulieu analysis method is used to analyze the piano music notation and electronic synthesis system module. For piano sheet music, sheet music recognition is the main problem in the whole system. Through the digital recognition method, the piano sheet music feature matrix is extracted to get the piano sheet music multiplication frequency points and the envelope function needs to be extracted for better electronic synthesis of piano sheet music. The envelope function can represent the relationship between piano sound intensity and time change and finally achieve the recognition of the piano score. We extract the music information from the digital score, thus converting the music information into MIDI files, reconstructing the score, and providing an audio carrier for the score transmission. The experimental results show that the system has a correct rate of 94.4% in extracting music information from piano scores, which can meet the needs of practical applications and provide a new way for music digital libraries, music education, and music theory analysis.

Evaluation of microenvironment cleanliness for computer assisted sperm analysis system based on fusion of neutrosophic feasures

Background and ObjectiveComputer assisted sperm analysis (CASA) provides a quantitative assessment of the quality of male fertility. But sometimes there are difficulties in comparing among diagnoses of different laboratories due to the deficiency of quality control indicators, especially for microenvironment cleanliness. An unclean microenvironment may seriously degrade the quality of microscopic images, resulting in diagnosis inaccuracy. The primary reason for the scarcity of microenvironment cleanliness indicators is non-sperm objects can't be recognized from microscopic images accurately. In this paper, to overcome the issues mentioned above and lay a foundation for CASA's quality control from the perspective of digital image processing, an algorithm for non-sperm objects extraction was designed and then an approach for evaluating of microenvironment cleanliness proposed. MethodFirst, two features neutrosophic grayscale and neutrosophic gradient were designed. Then based on them, a neutrosophic similarity measurement was developed. Combined with Otsu method, the measurement was applied to extract non-sperm objects by adjusting the value of noise factor. Finally, the indicator ratio of non-sperm objects (RNSO) was formed for evaluating the degree of microenvironment cleanliness. ConclusionThe experiments demonstrated non-sperm targets can be extracted efficiently and effectively by adjusting the noise factor of the proposed algorithm. The RNSO indicator can reflect microenvironment cleanliness degree properly and indicate whether the operations of sample production or video acquiring meet standards. So, it can provide convictive and quantitative information for CASA's quality control.

A novel outlier detection method for monitoring data in dam engineering

The accuracy of the dam monitoring data influences the evaluation of dam safety. However, the actual monitoring data may contain outliers due to sensor failure or operation mistakes, which consequences to an inaccurate evaluation. To tackle this issue, an universal and robust approach for outlier detection for monitoring data is proposed from the perspective of image processing and artificial intelligence in this work. The monitoring data series consists of isolated-pattern outliers, clustered-pattern outliers and normal points. We transform the monitoring data into a binary image of a scatter plot at first. With Gaussian blur, the gray scales of isolated points (outliers) are reduced and hence are eliminated after Otsu binarization. Then, Cuckoo Search (CS) algorithm is utilized to obtain the most possible connection of the pixel aggregations, distinguishing the clustered-pattern outliers and the process line automatically. To detect the outliers thoroughly, we implement the image processing techniques and CS algorithm cyclically until the detection results converge. The results indicate that the proposed method has the highest detection accuracy compared with other five outlier detection methods. Moreover, the monitoring model established based on the data pre-processed by the proposed method has better fitting and predicting ability.

Algorithm Policy for the Authentication of Indirect Fingerprints Used in Cloud Computing

User identity identification secures cloud computing. This study examined cloud service security authentication needs. Fingerprint recognition was used to create a new cloud security authentication system. The proposed system's design and process were thoroughly examined to secure cloud user data from unauthorized access. This study proposes a secure cloud server fingerprint match technique. Considering fingerprint uniqueness and stability, cloud security login authentication technology employing fingerprint recognition is researched to improve cloud services login security. Analyze the cloud security login system structure first. Next, fingerprint identification is explained. Finally, fingerprint identification of cloud security login systems is investigated from fingerprint registration, certification, fingerprint image processing perspectives, and a simple fingerprint image processing simulation. The results show that this login mechanism is secure and versatile. The biometric template is insecure, and stolen templates cannot be canceled, making user identity leaks easy. This work proposes indirect fingerprint authentication to address these issues. Finally, a thorough security analysis of the cloud computing method is offered.

Multitarget Vehicle Tracking and Motion State Estimation Using a Novel Driving Environment Perception System of Intelligent Vehicles

The multitarget vehicle tracking and motion state estimation are crucial for controlling the host vehicle accurately and preventing collisions. However, current multitarget tracking methods are inconvenient to deal with multivehicle issues due to the dynamically complex driving environment. Driving environment perception systems, as an indispensable component of intelligent vehicles, have the potential to solve this problem from the perspective of image processing. Thus, this study proposes a novel driving environment perception system of intelligent vehicles by using deep learning methods to track multitarget vehicles and estimate their motion states. Firstly, a panoramic segmentation neural network that supports end-to-end training is designed and implemented, which is composed of semantic segmentation and instance segmentation. A depth calculation model of the driving environment is established by adding a depth estimation branch to the feature extraction and fusion module of the panoramic segmentation network. These deep neural networks are trained and tested in the Mapillary Vistas Dataset and the Cityscapes Dataset, and the results showed that these methods performed well with high recognition accuracy. Then, Kalman filtering and Hungarian algorithm are used for the multitarget vehicle tracking and motion state estimation. The effectiveness of this method is tested by a simulation experiment, and results showed that the relative relation (i.e., relative speed and distance) between multiple vehicles can be estimated accurately. The findings of this study can contribute to the development of intelligent vehicles to alert drivers to possible danger, assist drivers’ decision-making, and improve traffic safety.

The role of film and television big data in real-time image detection and processing in the Internet of Things era

With the rapid development of the Internet, images play an increasingly critical role as an important data source in the Internet of Things (IoT). To meet the low-latency and high-efficiency transmission method of the IoT platform, the current problems in the image processing are analyzed from the perspective of real-time image processing in this study. The image features are extracted with the back propagation neural network (BPNN), and the images are classified with the support vector machines (SVM). A real-time image detection and processing platform (RT-IDPP) is constructed using the Adaboost framework based on the IoT, and the real-time image transmission and processing is realized based on different databases. It is found that the RT-IDPP proposed for the IoT realizes the image detection and tracking. The proposed method can not only run effectively on different cloud platforms for use, but also meet the real-time requirements in the image detection and tracking process, ensuring that the image detection rate is higher than 97%. Thus, the detection effect is better. Compared with the traditional image detection methods, the proposed method has higher detection rate and lower false-negative rate (FNR) and false-positive rate (FPR). The experimental detection effect on the film and television big data (FTBD) database is significantly better than that of other databases. This research can provide a theoretical basis for related researches on real-time image processing in the environment of IoT.

Ballistic Target Translation Compensation Based on Corner Detection Algorithm

During the mid-flight of the ballistic target, there are two movement modes: translational and micro-movement. The existence of translation will cause tilt and translation of the time-frequency curve of the scatter point echo, which will affect the extraction of ballistic missile micro-motion information. In response to this problem, this paper proposes a Shi-Tomasi corner detection algorithm from the perspective of image processing, which can effectively extract the corner information in the image. Afterwards, regression analysis is performed according to the obtained corner point information to realize the estimation of translational parameters. Through simulation experiments, it is concluded that the algorithm can better realize the compensation of translational components.

Determinantal Point Processes for Image Processing

Determinantal point processes (DPPs) are probabilistic models of configurations that favor diversity or repulsion. They have recently gained influence in the machine learning community, mainly because of their ability to elegantly and efficiently subsample large sets of data. In this paper, we consider DPPs from an image processing perspective, meaning that the data we want to subsample are pixels or patches of a given image. To this end, our framework is discrete and finite. First, we adapt their basic definition and properties to DPPs defined on the pixels of an image, that we call determinantal pixel processes (DPixPs). We are mainly interested in the repulsion properties of such a process and we apply DPixPs to texture synthesis using shot noise models. Finally, we study DPPs on the set of patches of an image. Because of their repulsive property, DPPs provide a strong tool to subsample discrete distributions such as that of image patches.