Multi-resolution Characteristics Research Articles

Haar wavelet for computing periodic responses of impact oscillators

Haar wavelets have an attractive property for non-smooth dynamical systems as they are capable of modelling sudden changes because of their local multi-resolution characteristics. In this paper, we applied the Haar wavelet collocation method embedding the segment technique to compute and detect periodic responses of an elastic impact oscillator. Comparisons between dynamical responses computed by a direct numerical simulation using a high accuracy Runge–Kutta algorithm and the proposed method are encouraging. Some key parameters of the impact oscillator were changed to demonstrate the effectiveness of the method. Moreover, the time–frequency index feature for the Haar wavelet coefficients describing impacts was proven to be an additional advantage of this method.

Real-World Non-Homogeneous Haze Removal by Sliding Self-Attention Wavelet Network

In complex natural haze scenes, image haze removal still faces significant challenges in removing non-homogeneous and dense haze. The double complexity of haze distribution, on the one hand, is reflected in the interference of haze to the global image information, and on the other hand, it is reflected in the imbalance of image brightness and color caused by random haze distribution. In natural scenes with prominent edge and texture features, the above problems may cause severe degradation of image quality and performance of various tasks. Numerous studies on network learning show that the effect of haze removal is closely related to haze feature expression. Therefore, to improve the performance of dehazing, this paper proposes a sliding self-attention wavelet network. Specifically, we first design a sliding self-attention module to identify haze regions in images and capture rich haze-related feature information. Then, considering the uneven distribution of haze in images, discrete wavelet transform (DWT) and inverse transform (IDWT) are used for constructing a hierarchical encoder-decoder structure, which can fully use the multi-resolution characteristics of DWT, locally decompose feature maps of different scales, extract low and high-frequency information, and then gradually recover sharp edges and precise texture details from hazy images. Finally, to enable the proposed network to generate more realistic haze-free images on different complex haze scenes, we develop a DWT-based adversarial loss function to constrain the low and high-frequency components of generated images closer to the corresponding clear images. Experimental results on the relevant public benchmark datasets show that the proposed algorithm achieves favorable dehazing performance.

Biorthogonal wavelet energy entropy method with different time windows for strong interference processing of GPR signals

In the light of the shielding effect of the strong reflection signal generated by the steel bars and the inhomogeneous of concrete materials on the weak signal in the tunnel lining detection, a high-resolution GPR image processing method of biorthogonal wavelet energy entropy in different time windows is proposed. Wavelet analysis has the characteristics of multiresolution, and wavelet entropy can represent the energy distribution characteristics of signals in different time windows with different scales. Firstly, the discrete biorthogonal wavelet transform is applied to decompose the original GPR image into subimages with different frequencies and different spatial local changes. Secondly, appropriate time windows are selected for wavelet entropy adaptive threshold processing of each subimage in the light of the different amplitude ratio of effective signal to strong interference in each subimage, and finally the reconstructed image after removing the strong reflection is spectrally whitened. Both the forward simulations and actual measurement results show that the method can effectively remove strong reflection interference, clear weak signals, and maintain the continuity of the reflection events, and compared to the conventional pulse deconvolution, predictive deconvolution and wavelet entropy methods with the same time window, this method has a more thorough interference removal effect, and the effective signal performance is more obvious, which proves the effectiveness and accuracy of the proposed method in improving the resolution of GPR image and highlighting weak signal.

Riesz-Laplace Wavelet Transform and PCNN Based Image Fusion

Important information perceived by human vision comes from the low-level features of the image, which can be extracted by the Riesz transform. In this study, we propose a Riesz transform based approach to image fusion. The image to be fused is first decomposed using the Riesz transform. Then the image sequence obtained in the Riesz transform domain is subjected to the Laplacian wavelet transform based on the fractional Laplacian operators and the multi-harmonic splines. After Laplacian wavelet transform, the image representations have directional and multi-resolution characteristics. Finally, image fusion is performed, leveraging Riesz-Laplace wavelet analysis and the global coupling characteristics of pulse coupled neural network (PCNN). The proposed approach has been tested in several application scenarios, such as multi-focus imaging, medical imaging, remote sensing full-color imaging, and multi-spectral imaging. Compared with conventional methods, the proposed approach demonstrates superior performance on visual effects, contrast, clarity, and the overall efficiency.

High-Accurate Quantitative Phase Imaging Based on the Transport of Intensity Equation and Wavelet Transform

We propose a high accurate quantitative phase imaging (QPI) method by using transport of intensity equation (TIE) and wavelet transform (WT). TIE provides a simple and fast method for QPI, but its accuracy is always limited due to the nonlinear error and noise problems caused by the defocus distance. To improve the accuracy of the phase recovered by TIE, WT is introduced to combine with TIE under several defocus distances. With the help of the multiresolution characteristics of the WT, effective information can be extracted from the phases retrieved with TIE at different defocus distances. As a result, a more accurate phase can be obtained by fusing this effective information. Moreover, to extend the applicability of our method, the problems of phase discrepancy and phase singularity in TIE are discussed and solved with an iterative WT-TIE algorithm. Numerical simulations and experiments with various types of phase maps are presented to comprehensively demonstrate the accuracy and effectiveness of our method.

Application of Lifting Wavelet Packet Decomposing Algorithm in EMC Simulation of Automobile

The purpose of this paper is to extract data features and denoise the interference excitation source in vehicle electromagnetic compatibility test. The lifting wavelet packet algorithm inherits the multi-resolution characteristics of the classic (first generation) wavelet transform. The transform is only carried out in the time domain, which can achieve in situ operation. It has the advantages of small space occupation, fast transformation speed, easy inversion, etc. It can use energy conservation criteria to extract characteristic energy to identify the conducted interference sources in the vehicle, and the obtained characteristic spectrum is used as the modulation array of the excitation source of the vehicle numerical simulation. In this paper, the collected interference signals are decomposed into lifting wavelet packets, and then the characteristic energy is extracted to identify the conducted interference sources in the vehicle. Signal to noise ratio (SNR), root mean square error (RMSE) and peak error (PE) are used to verify the consistency between the simulation signal and the original signal. The results show that the lifting wavelet packet algorithm has a strong ability to identify the conducted interference sources in the vehicle.

A Comparison of Raster-Based Forestland Data in Cropland Data Layer and the National Land Cover Database

The National Agricultural Statistics Service, the statistical arm of the US Department of Agriculture, and the Multi-Resolution Land Characteristics Consortium, a group of the US federal agencies, collect and publish several land-use and land-cover data sets. The aim of this study is to analyze the consistency of forestland estimates based on two widely used, publicly available products: the National Land-Cover Database (NLCD) and Cropland Data Layer (CDL). Both remote-sensing-based products provide raster-formatted land-cover categorization at a spatial resolution of 30 m. Although the processing of the yearly published CDL non-agricultural land-cover data is based on less frequently updated NLCD, the consistency of large-area forestland mapping between these two datasets has not been assessed. To assess the similarities and the differences between CDL- and NLCD-based forestland mappings for the state of North Carolina, we overlay the two data products for the years 2011 and 2016 in ArcMap 10.5.1 and analyze the location and attributes of the matched and mismatched forestland. We find that the mismatch is relatively smaller for the areas of the state where forests occupy larger shares of the total land, and that the relative mismatch is smaller in 2011 when compared to 2016. We also find that a large portion of the forestland mismatch is attributable to the dynamics of re-growth of periodically harvested and otherwise disturbed forests. Our results underscore the need for a holistic approach to data preparation, data attribution, and data accuracy when performing high-scale map-based analyses using each of these products.

Vermont Global Warming Solutions Act: The Costs of Inaction from Land Conversions

The Vermont (VT) Global Warming Solutions Act (GWSA, 2020) sets greenhouse gas (GHG) emissions reduction targets at 26% below 2005 by 2025, 40% below 1990 by 2030 and 80% below 1990 by 2050 for energy-related emissions only. Vermont’s omission of GHG emissions from land conversions could result in significant costs of inaction (COI), which could hinder the state’s mitigation and adaptation plans and result in climate crisis-related risks (e.g., credit downgrade). Science-based spatio-temporal data of GHG emissions from soils because of land conversions can be integrated into the conceptual framework of “action” versus “inaction” to prevent GHG emissions. The application of soil information data and remote sensing analysis can identify the GHG emissions from land conversions, which can be expressed as “realized” social costs of “inaction”. This study demonstrates the rapid assessment of the value of regulating ecosystems services (ES) from soil organic carbon (SOC), soil inorganic carbon (SIC), and total soil carbon (TSC) stocks, based on the concept of the avoided social cost of carbon dioxide (CO2) emissions for VT by soil order and county using remote sensing and information from the State Soil Geographic (STATSGO) and Soil Survey Geographic Database (SSURGO) databases. Classified land cover data for 2001 and 2016 were downloaded from the Multi-Resolution Land Characteristics Consortium (MRLC) website. These results provide accurate and quantitative spatio-temporal information about likely GHG emissions, which can be linked to VT’s climate action plan. A failure to considerably reduce emissions from land conversions would increase climate change costs and potential legal consequences for VT and beyond its borders.

Virtual wave field characteristic extractions based on multi-window time-sweep wave field transformation

The extraction of multi-scale and multi-resolution wave field characteristics is the focus of transient electromagnetic wave field transformation. However, isuues have been encountered in deep electromagnetic detection and multi-window time-sweep wave field transformation has become an important method for resolving these problems. In this study, a multi-window time-sweep wave field transformation method was adopted to perform the wave field transformations of high and low-resistivity thin layer models with different buried depths, along with different-sized 3D models with various buried depths. By analyzing the wave field characteristics, it is proven that for geological bodies with different burial depths, there must be an optimal window which renders the wave field characteristics of the time-sweep wave field transformation most apparent. In this study,multiple windows of different sizes are used for time-sweep wave field transformations to analyze the wave field characteristics of geological bodies at different scales and depths. Finally, the results of time-sweep wave field transformations of each window are correlated and superimposed to realize multi-resolution characteristics extraction of wave field of multi-scale geological bodies.

Multi-Spectral Image Fusion Using Wavelet Transform

The fusion of visible and infrared images of the same scene results in a single fused image that can provide a more detailed interpretation of the situation. With the help of energy compression and multiresolution characteristics of wavelets, which fuse the salient features such as edges and textures from source images without introducing any artifacts for context improvement and situational awareness, image fusion is more effective. This paper combines visible and infrared images (LWIR and NIR) using different wavelet transform methods on night-time imagery up to the 8th level of image decomposition. The performance of various wavelet techniques for multi-spectral image fusion was evaluated based on subjective and objective parameters. Additionally, Petrovic Metric parameters are used to measure the fusion performance.

Dual-angle rotation two-dimensional wavelet transform profilometry.

The two-dimensional continuous wavelet transform (2D CWT) has been widely used in single-shot fringe projection profilometry (FPP) because of its multi-resolution characteristics and anti-noise ability. This Letter aims to develop a novel 2D wavelet transform model in FPP. Dual-angle rotation operation on a 2D wavelet is performed to improve the directional selectivity and frequency localization of the wavelet. The proposed method improves the accuracy of the phase calculation of wavelet transform profilometry (WTP) for higher accuracy reconstruction from the single-shot fringe. We present mathematical expressions, theoretical analysis, and the comparison to traditional 2D WTP. The experiments demonstrate the effectiveness of the proposed method.

Blasting Vibration Signal Transform in Geotechnical Engineering Applied HHT and S Transform for Time-Frequency Analysis

Based on the same blasting vibration signal, the S transform and HHT method have been applied for the time and frequency analysis. Both the S transform and HHT method are analyzed and compared from the point of decomposition path and analysis function. It expressed that these two methods are complete and effective and have the characteristics of adaptive multiresolution. Especially, the time resolution of the HHT method is invariable and its accuracy is high. Moreover, its frequency resolution can be adaptively adjusted with the inherent characteristics of the signal. However, the time and frequency resolution of the S transform are limited by the uncertainty principle, and the resolution improvement is at the cost of reducing the resolution of another. Furthermore, the HHT method has much more adaptability and is superior to S transform in the analysis of nonstationary signals. In advance, the waveform curve and power spectral density of each detonator are extracted, which indicates that it is not scientific to use different segment detonators for the same circle of the auxiliary hole. Under the premise of the same charge quantity, the vibration energy peak value produced by the same segment detonator is less than that of several segments of detonators. Therefore, it is believed that the HHT method has broad application prospects in the study of wave propagation, attenuation law, structural dynamic response characteristics, and blasting vibration damage in rocks.

Climate Change Planning: Soil Carbon Regulating Ecosystem Services and Land Cover Change Analysis to Inform Disclosures for the State of Rhode Island, USA

The state of Rhode Island (RI) has established its greenhouse gas (GHG) emissions reduction goals, which require rapidly acquired and updatable science-based data to make these goals enforceable and effective. The combination of remote sensing and soil information data can estimate the past and model future GHG emissions because of conversion of “low disturbance” land covers (e.g., evergreen forest, hay/pasture) to “high disturbance” land covers (e.g., low-, medium-, and high-intensity developed land). These modeled future emissions can be used as a predevelopment potential GHG emissions information disclosure. This study demonstrates the rapid assessment of the value of regulating ecosystems services (ES) from soil organic carbon (SOC), soil inorganic carbon (SIC), and total soil carbon (TSC) stocks, based on the concept of the avoided social cost of carbon dioxide (CO2) emissions for RI by soil order and county using remote sensing and information from the State Soil Geographic (STATSGO) and Soil Survey Geographic Database (SSURGO) databases. Classified land cover data for 2001 and 2016 were downloaded from the Multi-Resolution Land Characteristics Consortium (MRLC) website. Obtained results provide accurate and quantitative spatio-temporal information about likely GHG emissions and show their patterns which are often associated with existing urban developments. These remote sensing tools could be used by the state of RI to both understand the nature of land cover change and likely GHG emissions from soil and to institute mandatory or voluntary predevelopment assessments and potential GHG emissions disclosures as a part of a climate mitigation policy.

Discrete wavelet transform processing system based on digital watermarking algorithm

This paper introduces the concept of multimedia digital watermarking and summarizes the current technology status. This paper summarizes the research status and basic principle of digital watermarking. The advantages of digital watermarking algorithm based on wavelet transform are analyzed. The proposed algorithm combines Arnold scrambling method and DWT transform multi-resolution characteristics, and adopts linear and different embedding strength methods. Effectively improve the security of the watermark. According to the classification results, different intensity watermarking components are embedded into the DCT low frequency coefficients of different image blocks. Experimental results show that this algorithm has strong masking and robustness, and the watermarking realized by using the proposed algorithm has good robustness against common image processing and noise interference.

Land Cover Change and Soil Carbon Regulating Ecosystem Services in the State of South Carolina, USA

Integration of land cover change with soil information is important for valuation of soil carbon (C) regulating ecosystem services (ES) and disservices (ED) and for site-specific land management. The objective of this study was to assess the change in value of regulating ES from soil organic carbon (SOC), soil inorganic carbon (SIC), and total soil carbon (TSC) stocks, based on the concept of the avoided social cost of carbon dioxide (CO2) emissions for the state of South Carolina (SC) in the United States of America (U.S.A.) by soil order (Soil Taxonomy), land cover, and land cover change (National Land Cover Database, NLCD) using information from the State Soil Geographic (STATSGO) and Soil Survey Geographic Database (SSURGO) databases. Classified land cover data for 2001 and 2016 were downloaded from the Multi-Resolution Land Characteristics Consortium (MRLC) website. The total estimated monetary mid-point value for TSC in the state of South Carolina was $124.42B (i.e., $124.42 billion U.S. dollars, where B = billion = 109) with the following monetary distribution in 2016 and percent change in value between 2001 and 2016: barren land ($259.7M, −9%) (i.e., $259.7 million U.S. dollars, where M = million = 106), woody wetlands ($33.8B, −1%), shrub/scrub ($3.9B, +9%), mixed forest ($6.9B, +5%), deciduous forest ($10.6B, −7%), herbaceous ($4.8B, −5%), evergreen forest ($28.6B, +1%), emergent herbaceous wetlands ($6.9B, −3%), hay/pasture ($7.3B, −10%), cultivated crops ($9.9B, 0%), developed, open space ($7.0B, +5%), developed, medium intensity ($978M, +46%), developed, low intensity ($2.9B, +15%), and developed, high intensity ($318M, +39%). The percent change in monetary values was different from percent change in areas because different soil orders have different TSC contents. The percent changes (between 2001 and 2016) both in areas and monetary values varied by soil order and land cover with $1.1B in likely “realized” social cost of C mostly associated with Ultisols ($658.8M). The Midlands region of the state experienced the highest gains in the “high disturbance” classes and corresponding SC-CO2 with over $421M for TSC, $354.6M for SOC, and $66.4M for SIC. Among counties, Horry County ranked first with over $142.2M in SC-CO2 for TSC, followed by Lexington ($103.7M), Richland ($95.3M), Greenville ($81.4M), York ($77.5M), Charleston ($70.7M), Beaufort ($64.1M), Berkeley ($50.9M), Spartanburg ($50.0M), and Aiken ($43.0M) counties. Spatial and temporal analyses of land cover can identify critical locations of soil carbon regulating ecosystem services at risk.

Classification using semantic feature and machine learning: Land-use case application

Land cover classification has interested recent works especially for deforestation, urban are monitoring and agricultural land use. Traditional classification approaches have limited accuracy especially for non-heterogeneous land cover. Thus, using machine may improve the classification accuracy. The presented paper deals with the land-use scene recognition on very high-resolution remote sensing imagery. We proposed a new framework based on semantic features, handcrafted features and machine learning classifiers decisions. The method starts by semantic feature extraction using a convolutional neural network. Handcraft features are also extracted based on color and multi-resolution characteristics. Then, the classification stage is processed by three learning machine algorithms. The final classification result performed by majority vote algorithm. The idea behind is to take advantages from semantic features and handcrafted features. The second scope is to use the decision fusion to enhance the classification result. Experimentation results show that the proposed method provides good accuracy and trustable tool for land use image identification.

Stepped Frequency Multiresolution Digital Signal Processing

With the rapid development of radar industry technology, the corresponding signal processing technology becomes more and more complex. For the radar with short-range detection function, its corresponding signal mostly presents the characteristics of wide bandwidth and multiresolution. In the traditional data processing process, a large number of signals will interfere with the signal, which makes the final signal processing difficult or even impossible. Based on this problem, this paper proposes a principal component linear prediction processing algorithm based on clutter suppression processing on the basis of traditional signal processing algorithm. According to the curve characteristics of the data returned by the target detected by the signal, through certain image signal measurement and transformation, the clutter can be effectively suppressed and the typical characteristics of the corresponding target curve can be enhanced. For the convergence problem of signal processing and the corresponding image chromatic aberration compensation problem, this paper will realize the chromatic aberration compensation of the corresponding target echo image based on the radial pointing transverse mode algorithm and enhance the convergence speed of the whole algorithm system. In the experimental part of this paper, the optimization algorithm proposed in this paper is compared with the traditional algorithm. The experimental results show that the algorithm proposed in this paper has obvious advantages in the convergence of signal processing and antijamming performance and has the promotion value.

Identification of wool and cashmere fibers based on multiscale geometric analysis

The identification of wool and cashmere fibers has always been an essential topic in the field of textile. In this work, a fiber recognition algorithm based on multiscale geometric analysis and deep convolutional neural network is introduced. As a new tool for signal analysis with multiscale and multi-direction, curvelet transform not only has multiscale and multi-resolution characteristics of the wavelet transform, but also has very strong directionality in the sense that it can provide an optimally sparse representation of fiber image with a large number of fiber contours information. The proposed method was based on multiscale geometric analysis to reduce the dimension of wool/cashmere fiber images and reduce the calculation of redundant data. The deep convolutional neural network was used to classify and recognize wool/cashmere fiber images. Then 400 fiber images of two kinds of fiber samples including wool and cashmere fibers, were collected respectively and processed by the methods including random interception and rotation to obtain a total of 600 fiber images respectively for the experiment analysis. The results show that the recognition accuracy is up to 96.67%. Compared with the traditional feature extraction and classification algorithm, this method dramatically improves model performance and identification accuracy.

Fractional order system modelling with Legendre wavelet multi-resolution analysis

This paper proposes a method of fractional order system (FOS) modelling with Legendre wavelet multi-resolution analysis. The proposed method expands the input and output signals of the system in the form of a Legendre wavelet, and constructs the Legendre wavelet integration operational matrix by use of a block-pulse function. To address the problem of the considerable volume of system identification data and system noise in practical engineering applications, the multi-resolution characteristics of the wavelet are combined to build a wavelet integration operational matrix from the multi-scale space. By continuously discarding the high-frequency information to reduce the length of the identification data, the identification speed of the system is accelerated and the influence of noise on the identification accuracy is reduced. In addition, the least squares method is used to find the optimal order in the identification interval and further accelerate the FOS modelling process. The proposed method rapidly identifies the FOS parameters with high accuracy, and is thus feasible for engineering applications. Its effectiveness is verified by simulation and photoelectric stabilized sighting platform experiment.

Speckle Noise Reduction in Sonar Image Based on Adaptive Redundant Dictionary

As acoustic waves are affected by the channel characteristics, such as scattering and reverberation when propagating in water, sonar images often exhibit speckle noise which will cause visual quality of the image to decrease. Therefore, denoising is a crucial preprocessing technique in sonar image applications. However, speckle noise is mainly caused by the sediment echo signals which are related to the background of seafloor sediment and can be obtained by prior modeling. Although deep learning-based denoising algorithms represent a research hotspot now, they are not suitable for such applications due to the high calculation amount and the large requirement of original images considering that sonar is carried by Autonomous Underwater Vehicles (AUVs) for collecting sonar images and performing calculation. In contrast, dictionary learning-based denoising method is more suitable and easier to be modeled. Compared with deep learning, it can greatly reduce the calculation amount and is more easily integrated into AUV systems. In addition, dictionary learning method based on image sparse representation can effectively achieve image denoising similarly. In order to solve the above problems, we propose a new adaptive dictionary learning method based on multi-resolution characteristics, which combines K-SVD dictionary learning with wavelet transform. Our method has the characteristics of dictionary learning and inherits the features of wavelet analysis as well. Compared with several classical methods, the proposed method is better at speckle noise reduction and edge detail preservation. At the same time, the calculation time is greatly reduced and the efficiency is significantly improved.