Blind Deconvolution Algorithm Research Articles

High-noise solar panel defect identification method based on the improved EfficientNet-V2

As a crucial element in photovoltaic power generation systems, the condition of solar panels significantly impacts the efficiency of power generation. The ability to accurately and promptly detect defects in solar panels is essential for enhancing system performance. This study introduces a novel model for identifying defects in photovoltaic modules, leveraging an enhanced version of EfficientNet-V2. This model aims to address challenges in identifying defects in infrared images of solar panels under conditions of high-noise and low-model efficiency. To address the challenges of high image noise and blur, this article initially presents a methodology that combines the Db4 wavelet transform with a blind deconvolution algorithm for comprehensive preprocessing of the original image. Furthermore, this study optimizes the model's feature representation capabilities by implementing key transformations within the EfficientNet-V2 network framework. Notably, we replaced the traditional SE block with the more efficient channel attention (ECA) mechanism module. Due to its lightweight structure and effective performance, ECA substantially improves the model's capacity to extract complex and abstract image features, while also accelerating the training process's convergence speed and enhancing overall computational efficiency. At the classifier level, this paper innovatively integrates the XGBoost ensemble learning algorithm into the model, substituting the conventional softmax classifier used in traditional convolutional neural network (CNN). With its superior generalization capabilities, robust nonlinear modeling skills, and efficient computational characteristics, XGBoost can more accurately detect minute defects in solar panels based on the deep features produced by EfficientNet-V2, thereby significantly improving the accuracy and robustness of defect detection. Simulation results demonstrate that the proposed model structure outperforms traditional CNN models in terms of accuracy and stability, underscoring the efficacy of the enhanced EfficientNet-V2 model in detecting solar panel defects under high-noise conditions.

Blind and robust estimation of adaptive optics point spread function and diffuse halo with sharp-edged objects

Context. Initially designed to detect and characterise exoplanets, extreme adaptive optics (AO) systems open a new window onto the Solar System by resolving its small bodies. Nonetheless, their study remains limited by the accuracy of the knowledge of the AO-corrected point spread function (AO-PSF) that degrades their image and produces a bright halo, potentially hiding faint moons in their close vicinity. Aims. To overcome the random nature of AO-PSFs, I aim to develop a method that blindly recovers the PSF and its faint structured extensions directly into the data of interest, without any prior on the instrument or the object’s shape. The objectives are both to deconvolve the object and to properly estimate and remove its surrounding halo to highlight potential faint companions. Methods. My method first estimated the PSF core via a parametric model fit, under the assumption of a sharp-edged flat object. Then, the resolved object and the PSF extensions were alternatively deconvolved with a robust method, insensitive to model outliers, such as cosmic rays or unresolved moons. Finally, the complex halo produced by the AO system was modelled and removed from the data. Results. The method is validated on realistic simulations with an on-sky AO-PSF from the SPHERE/ZIMPOL instrument. On real data, the proposed blind deconvolution algorithm strongly improves the image sharpness and retrieves details on the surface of asteroids. In addition, their moons are visible in all tested epochs despite important variability in turbulence conditions. Conclusions. My method shows the feasibility of retrieving the complex features of AO-PSFs directly from the data of interest. It paves the way towards more precise studies of asteroid surfaces and the discovery and characterisation of Solar System moons in archival data or with future instruments on extremely large telescopes with ever more complex AO-PSFs.

A novel adaptive blind deconvolution algorithm: application to feature extraction of weak faults in RV reducer gears

Abstract Aiming at the problem that the non-stationary and nonlinear weak fault signal of RV (rotate vector) reducers is hard to extract fault features due to the influence of noise and transmission paths, as well as the selection of parameters for maximum correlation kurtosis deconvolution (MCKD) relies heavily on manual experience, this article proposes a fault feature extraction method based on parameter adaptive MCKD for the gear faults of RV reducers. Firstly, the sparrow search algorithm combining sine-cosine and Cauchy mutation (SCSSA) is used to adaptively search for the input parameters of MCKD and obtain the signal after deconvolution with the optimal parameters. Secondly, the deconvoluted signal is subjected to ensemble empirical mode decomposition to obtain modal components on different frequency bands. Finally, calculate the multi-scale fuzzy entropy (MFE) of each component, constructing a MFE feature set vector, and input the feature vector into the support vector machine for fault classification and recognition. The experimental analysis and verification results both indicate that the proposed method can adaptively enhance the weak impact components in the gear signals of the RV reducer, effectively extracting weak fault features disturbed by noise. Compared with minimum entropy deconvolution, multipoint optimal minimum entropy deconvolution adjusted and MCKD, the proposed method has improved identification rate by 17.50%, 10.63% and 15.63%, respectively. In addition, in comparison to multiverse optimization and particle swarm optimizatio algorithms, the SCSSA exhibits superior performance when optimizing MCKD parameters, offering faster convergence speed, higher accuracy, and greater robustness.

Solar multiobject multiframe blind deconvolution with a spatially variant convolution neural emulator

The study of astronomical phenomena through ground-based observations is always challenged by the distorting effects of Earth's atmosphere. Traditional methods of post facto image correction, essential for correcting these distortions, often rely on simplifying assumptions that limit their effectiveness, particularly in the presence of spatially variant atmospheric turbulence. Such cases are often solved by partitioning the field of view into small patches, deconvolving each patch independently, and merging all patches together. This approach is often inefficient and can produce artifacts. Recent advancements in computational techniques and the advent of deep learning offer new pathways to address these limitations. This paper introduces a novel framework leveraging a deep neural network to emulate spatially variant convolutions, offering a breakthrough in the efficiency and accuracy of astronomical image deconvolution. By training on a dataset of images convolved with spatially invariant point spread functions and validating its generalizability to spatially variant conditions, this approach presents a significant advancement over traditional methods. The convolution emulator is used as a forward model in a multiobject multiframe blind deconvolution algorithm for solar images. The emulator enables the deconvolution of solar observations across large fields of view without resorting to patch-wise mosaicking, thus avoiding the artifacts associated with such techniques. This method represents a significant computational advantage, reducing processing times by orders of magnitude.

Improvement of Whole-body Bone Planar Images on a Bone-dedicated Single-photon Emission Computed Tomography Scanner by Blind Deconvolution Algorithm.

We have developed a bone-dedicated collimator with higher sensitivity but slightly degraded resolution on single-photon emission computed tomography (SPECT) for planar bone scintigraphy, compared with conventional low-energy high-resolution collimator. In this work, we investigated the feasibility of using the blind deconvolution algorithm to improve the resolution of planar images on bone scintigraphy. Monte Carlo simulation was performed with the NCAT phantom for modeling bone scintigraphy on the clinical dual-head SPECT scanner (Imagine NET 632, Beijing Novel Medical Equipment Ltd.) equipped with the bone-dedicated collimator. Maximum likelihood estimation method was used for the blind deconvolution algorithm. The initial estimation of point spread function (PSF) and iteration number for the method were determined by comparing the deblurred images obtained from different input parameters. We simulated different tumors in five different locations and with five different diameters to evaluate the robustness of the initial inputs. Furthermore, we performed chest phantom studies on the clinical SPECT scanner. The quantified increased contrast ratio (CR) between the tumor and the background was evaluated. The 2 mm PSF kernel and 10 iterations provided a practical and robust deblurred image on our system. Those two inputs can generate robust deblurred images in terms of the tumor location and size with an average increased CR of 21.6%. The phantom studies also demonstrated the ability of blind deconvolution, using those two inputs, with increased CRs of 17%, 17%, 22%, 20%, and 13% for lesions with diameters of 1 cm, 2 cm, 3 cm, 4 cm, and 5 cm, respectively. It is feasible to use the blind deconvolution algorithm to deblur the planar images for SPECT bone scintigraphy. The appropriate values of the PSF kernel and the iteration number for the blind deconvolution can be determined using simulation studies.

Restoration of Radiographic Neutron Image Using Single-Channel Blind Deconvolution

This paper addresses the challenge of accurately estimating the 2D Point Spread Function (PSF) or blur kernel in neutron radiography, where traditional methods, such as the Edge Spread Function (ESF), prove time-consuming and reliant on manual edge selection. The proposed alternative introduces a robust sparse image prior known as the enhanced Patch-Wise Intensity (EPI) image prior in a singlechannel blind deconvolution algorithm, avoiding the need for intricate devices like pinhole or slit phantoms. Leveraging regularization and optimization techniques, the algorithm efficiently estimates PSF in a single image through a multilayer iterative alternating approach. The study aims to enhance PSF accuracy, leading to a more accurate solution to the neutron image restoration problem. Comparative results with real neutron images indicate the proposed method outperforms ESF, demonstrating improved overall image quality both visually and quantitatively in terms of blind/no reference evaluation (BRISQUE).

A Novel Online Sliding Blind Deconvolution Algorithm for Satellite Microvibration Source Separation in Time-Varying Environment

A Novel Online Sliding Blind Deconvolution Algorithm for Satellite Microvibration Source Separation in Time-Varying Environment

Image defocus deblurring method based on gradient difference of boundary neighborhood

BackgroundFor static scenes with multiple depth layers, the existing defocused image deblurring methods have the problems of edge ringing artifacts or insufficient deblurring degree due to inaccurate estimation of blur amount, In addition, the prior knowledge in non blind deconvolution is not strong, which leads to image detail recovery challenge. MethodsTo this end, this paper proposes a blur map estimation method for defocused images based on the gradient difference of the boundary neighborhood, which uses the gradient difference of the boundary neighborhood to accurately obtain the amount of blurring, thus preventing boundary ringing artifacts. Then, the obtained blur map is used for blur detection to determine whether the image needs to be deblurred, thereby improving the efficiency of deblurring without manual intervention and judgment. Finally, a non blind deconvolution algorithm is designed to achieve image deblurring based on the blur amount selection strategy and sparse prior. ResultsExperimental results show that our method improves PSNR and SSIM by an average of 4.6% and 7.3%, respectively, compared to existing methods. ConclusionsExperimental results show that our method outperforms existing methods. Compared with existing methods, our method can better solve the problems of boundary ringing artifacts and detail information preservation in defocused image deblurring.

Source Quantitative Identification by Reference-Based Cubic Blind Deconvolution Algorithm

The semi-blind deconvolution algorithm improves the separation accuracy by introducing reference information. However, the separation performance depends largely on the construction of reference signals. To improve the robustness of the semi-blind deconvolution algorithm to the reference signals and the convergence speed, the reference-based cubic blind deconvolution algorithm is proposed in this paper. The proposed algorithm can be combined with the contribution evaluation to provide trustworthy guidance for suppressing satellite micro-vibration. The normalized reference-based cubic contrast function is proposed and the validity of the new contrast function is theoretically proved. By deriving the optimal step size of gradient iteration under the new contrast function, we propose an efficient adaptive step optimization method. Furthermore, the contribution evaluation method based on vector projection is presented to implement the source contribution evaluation. Numerical simulation analysis is carried out to validate the availability and superiority of this method. Further tests given by the simulated satellite experiment and satellite ground experiment also confirm the effectiveness. The signals of control moment gyroscope and flywheel were extracted, respectively, and the contribution evaluation of vibration sources to the sensitive load area was realized. This research proposes a more accurate and robust algorithm for the source separation and provides an effective tool for the quantitative identification of the mechanical vibration sources.

Resolution enhancement of 2D controlled-source electromagnetic images by use of point-spread function inversion

The marine controlled-source electromagnetic technique is employed both in large-scale geophysical applications as well as within the exploration of hydrocarbons and gas hydrates. Because of the diffusive character of the EM field, only very low frequencies are used, leading to inversion results with low resolution. In this paper, we calculated the resolution matrix associated with the inversion and derived the corresponding point-spread functions. The PSFs provided information about how much the actual inversion was blurred. Using a space-varying deconvolution can thus further improve the inversion result. The actual deblurring was carried out using the nonnegative flexible conjugate gradient least-squares (NN-FCGLS) algorithm, which is a fast iterative restoration technique. To attain completeness, we also introduced the results obtained using a blind deconvolution algorithm based on the maximum likelihood estimation with unknown PSFs. The potential of the proposed approach has been demonstrated using both complex synthetic data and field data acquired at the Wisting oil field in the Barents Sea. In both cases, the resolution of the final inversion result was improved and showed greater agreement with the known target area.

Blind Deconvolution Based on Correlation Spectral Negentropy for Bearing Fault.

Blind deconvolution is a method that can effectively improve the fault characteristics of rolling bearings. However, the existing blind deconvolution methods have shortcomings in practical applications. The minimum entropy deconvolution (MED) and the optimal minimum entropy deconvolution adjusted (OMEDA) are susceptible to extreme values. Furthermore, maximum correlated kurtosis deconvolution (MCKD) and multipoint optimal minimum entropy deconvolution adjusted (MOMEDA) are required prior knowledge of faults. On the basis of the periodicity and impact of bearing fault signals, a new deconvolution algorithm, namely one based on maximum correlation spectral negentropy (CSNE), which adopts the particle swarm optimization (PSO) algorithm to solve the filter coefficients, is proposed in this paper. Verified by the simulated vibration model signal and the experimental simulation signal, the PSO-CSNE algorithm proposed in this paper overcomes the influence of harmonic signals and random pulse signals more effectively than other blind deconvolution algorithms when prior knowledge of the fault is unknown.

Leak Location for Urban Elbowed Water Pipe Based on Complex-Optimized FastICA Blind Deconvolution

Previous research has primarily focused on leak diagnosis algorithms under the interference of outside environment noise. However, the former algorithms in leak detection are easily affected by noise from the pipe, especially the one that contains elbow noise. This article was mainly concerned with the effective and reliable approach to precisely locate leaks in the buried elbow pipeline system. To overcome the drawback of elbow noise interference and extract the leak source from the mixing signal in the greatest extent, this article proposes a novel frequency-domain-independent component analysis (ICA) blind deconvolution algorithm, which is based on a popular optimized FastICA (O-FICA) called complex-optimized FastICA (CO-FICA) algorithm. In comparison, CO-FICA differs from the time-domain blind convolution separation (T-BCS) algorithm, which transforms complicated time-domain convolution into a simple frequency-domain product problem. Compared with the conventional complex FastICA (C-FICA), the proposed CO-FICA method does not need to estimate signal statistics at each step, but it displays more excellent performance in convergent speed and separated precision. Altogether, the results from this study demonstrate that the localization precision of the proposed method is <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\approx 87\%$ </tex-math></inline-formula> under the interference of elbow noise.

Multiplane Image Restoration Using Multivariate Curve Resolution: An Alternative Approach to Deconvolution in Conventional Brightfield Microscopy

Three-dimensional reconstruction in brightfield microscopy is challenging since a 2D image includes from in-focus and out-of-focus light which removes the details of the specimen’s structures. To overcome this problem, many techniques exist, but these generally require an appropriate model of Point Spread Function (PSF). Here, we propose a new images restoration method based on the application of Multivariate Curve Resolution (MCR) algorithms to a stack of brightfield microscopy images to achieve 3D reconstruction without the need for PSF. The method is based on a statistical reconstruction approach using a self-modelling mixture analysis. The MCR-ALS (ALS for Alternating Least Square) algorithm under non-negativity constraints, Wiener, Richardson–Lucy, and blind deconvolution algorithms were applied to silica microbeads and red blood cells images. The MCR analysis produces restored images that show informative structures which are not noticeable in the initial images, and this demonstrates its capability for the multiplane reconstruction of the amplitude of 3D objects. In comparison with 3D deconvolution methods based on a set of No Reference Images Quality Metrics (NR-IQMs) that are Standard Deviation, ENTROPY Average Gradient, and Auto Correlation, our method presents better values of these metrics, showing that it can be used as an alternative to 3D deconvolution methods.

Unrolled Variational Bayesian Algorithm for Image Blind Deconvolution.

In this paper, we introduce a variational Bayesian algorithm (VBA) for image blind deconvolution. Our VBA generic framework incorporates smoothness priors on the unknown blur/image and possible affine constraints (e.g., sum to one) on the blur kernel, integrating the VBA within a neural network paradigm following an unrolling methodology. The proposed architecture is trained in a supervised fashion, which allows us to optimally set two key hyperparameters of the VBA model and leads to further improvements in terms of resulting visual quality. Various experiments involving grayscale/color images and diverse kernel shapes, are performed. The numerical examples illustrate the high performance of our approach when compared to state-of-the-art techniques based on optimization, Bayesian estimation, or deep learning.

Algorithms for Sparse Multichannel Blind Deconvolution

In this paper we present two algorithms for sparse multichannel blind deconvolution. The first algorithm is based on a cascade of a forward and a backward prediction error filter (C-PEF). The second consists in an alternating minimization algorithm for estimating both the reflectivity series and the seismic wavelet (AM-SMBD). We also compare the algorithms with other state-of-the-art sparse blind deconvolution algorithms. Simulation results with synthetic data for different SNR levels showed that the AM-SMBD was outperformed (in terms of the Pearson Correlation Coefficient and the Gini Correlation Coefficient) other estimation methods, like the reduced SMBD, the TS-sparseTLS (Toeplitz-structured sparse total least square) and the SMBD-SPG (Sparse multichannel blind deconvolution via spectral projected-gradient). For the same data, the C-PEF was able to provide better results (in terms of the Gini Correlation Coefficient, visual inspection and frequency gain) when compared with the F-SMBD (Fast sparse multichannel blind deconvolution). In a simulation considering reflectivities with different levels of sparsity, the C-PEF seems to be more robust for less sparse data when compared with AM-SMBD and SMBD-SPG (up to a certain degree of sparsity). Finally, simulations considering a real land acquisition show that both algorithms were able to greatly improve the resolution of the seismic data.

Vertebral and ribs enhancement in Tc-99m methylene diphosphonate bone scan image by blind deconvolution algorithm.

The objective of the study was to restore Tc-99m methylene diphosphonate (MDP) bone scan image using blind deconvolution (BD) algorithm so that ribs, vertebrae, and lesions present in them become prominent. Our study consists of retrospective data in which 356 Tc-99m MDP bone scan images (178 anterior and 178 posterior) were processed using dynamic stochastic resonance algorithm, block-matching 3D filter, and then restored using BD algorithm. Two nuclear medicine (NM) physicians compared restored image with its input image; they especially lookedfor: (a) improvement in lesions detectability, (b) artifacts if any, (c) deterioration in ribs and vertebra, and (d) contrast enhancement in adjacent vertebra and adjacent ribs. They selected one out of two (restored and input) images, which had better quality. The overall image quality was also assessed using the following image quality metrics: brightness, blur, global contrast factor, and contrast per pixel. The Wilcoxon signed-rank test was applied for finding significant difference between the value of image quality metrics of restored image and input image at level of significance alpha = 0.05. According to NM physicians, 80.3% (286 out of 356) of restored images were acceptable, whereas 19.6% (70 out of 356) were unacceptable. Ribs and vertebrae were prominent in 161 out of 178 posterior restored images. Lumbar vertebrae were enhanced and well differentiated from adjacent vertebrae in 125 out of 178 anterior restored images. The value of image quality metrics of restored and input images were found to be significantly different ( P -value < 0.0001). Ribs, vertebrae, and lesions present in them become prominent in the most of Tc-99m MDP bone scan images (80.3%) restored using BD algorithm.

Fast nonlinear blind deconvolution for rotating machinery fault diagnosis

Sparse optimization based early fault diagnosis method is drawing more and more attention. In these methods, the objective function is usually a sparsity measure which can represent the impulsive signature produced by the mechanical impacts between rotating components. Therefore, the key point is the fault representation and convergence stability of the objective function. Inspired by this, fast nonlinear blind deconvolution algorithm is proposed for early fault diagnosis of rotating machinery. First, sigmoid function is developed to the generalized form to improve the fault representation ability of the objective function under noisy environment. The nonlinear mapping and sparse expression ability are discussed in detail. Then, Gaussian fitting window function and L1/2 penalty of filter are used to improve the distribution and performance of the weight vector. Finally, simulation data and early bearing fault data are employed to verify the effectiveness and determine optimal parameter setting of the proposed method. Based on the comparison results with the existing methods, the proposed method is found to be a promising method for the early-stage fault diagnosis, which can significantly improve the noise adaptability, computation effectiveness and robustness of fault diagnosis.

Nonstationary seismic reflectivity inversion based on prior-engaged semisupervised deep learning method

Reflectivity inversion methods based on a stationary convolution model are essential for seismic data processing. They compress the seismic wavelet, and by broadening the bandwidth of seismic data, they assist the interpretation of seismic sections. Unfortunately, they do not apply to realistic nonstationary deconvolution cases in which the seismic wavelet varies as it propagates in the subsurface. Deep learning techniques have been proposed to solve inverse problems in which networks can behave as the regularizer of the inverse problem. Our goal is to adopt a semisupervised deep learning approach to invert reflectivity when the propagating wavelet is considered unknown and time-variant. To this end, we have designed a prior-engaged neural network by unrolling an alternating iterative optimization algorithm, in which convolutional neural networks are used to solve two subproblems. One is to invert the reflectivity, and the other is to estimate the time-varying wavelets. In general, it is well known that, when working with geophysical inverse problems such as ours, one has limited access to labeled data for training the network. We circumvent the problem by training the network via a data-consistency cost function in which seismic traces are honored. Reflectivity estimates also are honored at spatial coordinates in which true reflectivity series derived from borehole data are available. The cost function also penalizes time-varying wavelets from varying abruptly along the spatial direction. Experiments are conducted to find the effectiveness of our method. We also compare our approach to a nonstationary blind deconvolution algorithm based on regularized inversion. Our findings reveal that the developed method improves the vertical resolution of seismic sections with noticeable correlation coefficient improvements over the nonstationary blind deconvolution. In addition, our method is less sensitive to initial estimates of nonstationary wavelets. Moreover, it needs less human intervention when setting parameters than regularized inversion.

Fast Multimodulus Blind Deconvolution Algorithms

A novel class of fast Multi-Modulus algorithms (fastMMA) for Blind Source Separation (BSS) and deconvolution are presented in this work. These are obtained through a fast fixed-point optimization rule used to minimize the Multi-Modulus (MM) criterion. Here, two BSS versions are provided to separate the sources either by finding the separation matrix at once or by separating a single source each time using a fast deflation technique. Further, the latter method is extended to cover systems of convolutive nature. Interestingly, these algorithms are implicitly shown to belong to the fixed step-size gradient descent family, henceforth, an algebraic variable step-size is proposed to make these algorithms converge even much faster. Apart from being computationally and performance-wise attractive, the new algorithms are free of any user-defined parameters.

Minimum noise amplitude deconvolution and its application in repetitive impact detection

Blind deconvolution (BD) is an effective technology for rotating machinery fault detection because it significantly weakens noise and reduces the interference of the system transmission path. This paper analyses the possible shortcomings of several popular BDs in detecting repetitive impacts hidden in rotating machinery signals. Then a new index named periodic noise amplitude ratio (PNAR) is defined to measure the noise level of fault signals. Minimum noise amplitude deconvolution (MNAD) is a new BD algorithm based on PNAR minimization. Unlike the existing BDs, MNAD does not focus on the impacts with certain randomness in their moments of occurrence but enhances the repetitive impact characteristics by weakening the defined periodic noise. MNAD is particularly suitable for detecting repetitive impacts hidden in cyclostationary signals. Simulation analysis and experimental results show that MNAD adaptively determines one or more resonance bands generated by repetitive impacts and has a good suppression effect on the noise in the resonance bands. The implementation of MNAD is available in MATLAB community.