Reconstruction Performance Research Articles

TRFSA-HQS: Transformer-based MRI Compressed Sensing Network with Frequency Domain Self-Attention

Compressive sensing (CS) can reconstruct undersampled magnetic resonance images, but its iterative optimization process tends to be computationally intensive and time-consuming. Convolutional neural networks (CNNs) have demonstrated impressive reconstruction performance through non-linear feature extraction and mapping capabilities. However, CNNs often struggle to effectively learn and capture global dependencies in the data. Therefore, constructing a MRI reconstruction method that meets clinical real-time imaging and captures dynamic global correlation is crucial in fast and high-precision MRI tasks. We propose a deep unfolding network (TRFSA-HQS) for fast and accurate CS reconstruction, which combines frequency domain self-attention (FSA) based Transformer and half-quadratic splitting (HQS) iterative optimization scheme. TRFSA-HQS adopts an iterative scheme based on HQS to effectively decouple and update optimization problems. The decoupled data subproblems are updated by minimizing the objective function with competitive terms, while the regularization subproblems are updated using the TRFSA deep prior network. The TRFSA module employs an asymmetric UNet architecture, where the encoder and decoder utilize a frequency-domain discriminative feedforward network (DFFN) and FSA. The DFFN selectively extracts deep features from different frequency components, while the FSA captures global dependencies in the frequency domain. The experiment demonstrate that the proposed model achieves an average reconstruction PSNR of 35.11dB on a 0.2 sampling rate test set on FastMRI knee joint data, with an inference time of 0.74s. It has good reconstruction performance and noise robustness, meeting the clinical real-time imaging requirements.

Seismic Data Reconstruction Via A Hybrid CNN-Low-Rank Method

Convolutional Neural Networks (CNNs) have shown promising results in seismic data reconstruction due to their exceptional ability to extract local features. However, incomplete seismic data may impede CNNs’ ability to discern meaningful features, leading to a degradation of reconstruction performance. Therefore, we propose a hybrid CNN-Low-Rank (CNN-LR) method which leverages Low-Rank (LR) techniques to recover data structure information, followed by detail refinement using a plug-and-play (PnP) trained CNN within an iterative framework. This approach utilizes LR techniques to restore inherent structural information, compensating for the absence of local information, thereby enabling CNNs to infer missing traces more accurately. Specifically, we first train a network to learn the mapping from low-rank recovered data patches with randomly selected rank parameters to complete data patches. Next, we incorporate the PnP trained CNN into an iterative framework, where low-rank recovery and network refinement are interleaved within each iteration. This creates a continuous reconstruction process until a termination condition is met. Performance evaluations of our proposed algorithm, conducted on synthetic and field datasets with a high missing trace ratio, demonstrate its superiority over the traditional low-rank method (multi-channel singular spectrum analysis, MSSA) and CNN-based method (U-net). Moreover, our approach shows strong performance in reconstructing large contiguous gaps and handling higher-dimensional seismic data (including 3D and 5D), broadening its applicability and robustness in practical seismic data reconstruction tasks.

Deep Neural Networks for Estimating Regularization Parameter in Sparse Time–Frequency Reconstruction

Time–frequency distributions (TFDs) are crucial for analyzing non-stationary signals. Compressive sensing (CS) in the ambiguity domain offers an approach for TFD reconstruction with high performance, but selecting the optimal regularization parameter for various signals remains challenging. Traditional methods for parameter selection, including manual and experimental approaches, as well as existing optimization procedures, can be imprecise and time-consuming. This study introduces a novel approach using deep neural networks (DNNs) to predict regularization parameters based on Wigner–Ville distributions (WVDs). The proposed DNN is trained on a comprehensive dataset of synthetic signals featuring multiple linear and quadratic frequency-modulated components, with variations in component amplitudes and random positions, ensuring wide applicability and robustness. By utilizing DNNs, end-users need only provide the signal’s WVD, eliminating the need for manual parameter selection and lengthy optimization procedures. Comparisons between the reconstructed TFDs using the proposed DNN-based approach and existing optimization methods highlight significant improvements in both reconstruction performance and execution time. The effectiveness of this methodology is validated on noisy synthetic and real-world signals, emphasizing the potential of DNNs to automate regularization parameter determination for CS-based TFD reconstruction in diverse signal environments.

Revealing the Dark Side of Non-Local Attention in Single Image Super-Resolution.

Single Image Super-Resolution (SISR) aims to reconstruct a high-resolution image from its corresponding low-resolution input. A common technique to enhance the reconstruction quality is Non-Local Attention (NLA), which leverages self-similar texture patterns in images. However, we have made a novel finding that challenges the prevailing wisdom. Our research reveals that NLA can be detrimental to SISR and even produce severely distorted textures. For example, when dealing with severely degrade textures, NLA may generate unrealistic results due to the inconsistency of non-local texture patterns. This problem is overlooked by existing works, which only measure the average reconstruction quality of the whole image, without considering the potential risks of using NLA. To address this issue, we propose a new perspective for evaluating the reconstruction quality of NLA, by focusing on the sub-pixel level that matches the pixel-wise fusion manner of NLA. From this perspective, we provide the approximate reconstruction performance upper bound of NLA, which guides us to design a concise yet effective Texture-Fidelity Strategy (TFS) to mitigate the degradation caused by NLA. Moreover, the proposed TFS can be conveniently integrated into existing NLA-based SISR models as a general building block. Based on the TFS, we develop a Deep Texture-Fidelity Network (DTFN), which achieves state-of-the-art performance for SISR. Our code and a pre-trained DTFN are available on GitHub† for verification.

DSU-Net: A Dynamic Stage Unfolding Network for high-noise image compressive sensing denoising

Deep unfolding networks (DUNs) have demonstrated considerable efficacy in the domain of compressive sensing (CS), attributed to their superior performance and interpretability. Nonetheless, many existing CS methodologies fail to account for the influence of noise prior to image sampling, resulting in blurring and distortion in the reconstructed images. To address these issues, this paper proposes a dynamic stage unfolding network (DSUNet). Firstly, a novel dynamic stage unfolding mechanism is proposed to achieve feature refinement by dynamically optimizing various noisy images in a sequential manner. Secondly, a step inertia fusion module (SIFM) is developed to execute multi-tiered information fusion from adjacent stages, thereby promoting feature reuse and minimizing the loss of detailed information. Finally, a step cross transformer denoiser (SCTD) is designed to capture correlations between distant pixels, effectively addressing the limitations associated with local attributes and significantly improving image reconstruction performance. Comprehensive experimental evaluations indicate that the proposed DSUNet achieves outstanding performance under both low CS ratios and high noise levels, successfully addressing the challenges of denoising and reconstruction in high-resolution and high-noise images. Codes are available at https://github.com/zzuli407/DSU-Net.

From data to dynamics: Reconstructing soliton collision phenomena in optical fibers using a convolutional autoencoder

In this study, a convolutional autoencoder is constructed to extract and reconstruct the dynamical processes of soliton collisions in optical fibers. The model demonstrates exceptional reconstruction capabilities, accurately capturing the evolution of optical event horizons and reproducing nonlinear phenomena such as complex frequency conversions and energy exchange processes. The reconstruction results show high consistency with the numerical simulations, with RMSE values of 0.0220 and 0.0174 in the temporal and frequency domains, respectively. Additionally, by adjusting the training parameters of the convolutional autoencoder model, its reconstruction performance for nonlinear dynamic processes was further validated. This method is expected to provide a different perspective for studying nonlinear phenomena in optical fibers while reducing the consumption of computational resources.

An improved low-rank plus sparse unrolling network method for dynamic magnetic resonance imaging.

Recent advances in deep learning have sparked new research interests in dynamic magnetic resonance imaging (MRI) reconstruction. However, existing deep learning-based approaches suffer from insufficient reconstruction efficiency and accuracy due to the lack of time correlation modeling during the reconstruction procedure. Inappropriate tensor processing steps and deep learning models may lead to not only a lack of modeling in the time dimension but also an increase in the overall size of the network. Therefore, this study aims to find suitable tensor processing methods and deep learning models to achieve better reconstruction results and a smaller network size. We propose a novel unrolling network method that enhances the reconstruction quality and reduces the parameter redundancy by introducing time correlation modeling into MRI reconstruction with low-rank core matrix and convolutional long short-term memory (ConvLSTM) unit. We conduct extensive experiments on AMRG Cardiac MRI dataset to evaluate our proposed approach. The results demonstrate that compared to other state-of-the-art approaches, our approach achieves higher peak signal-to-noise ratios and structural similarity indices at different accelerator factors with significantly fewer parameters. The improved reconstruction performance demonstrates that our proposed time correlation modeling is simple and effective for accelerating MRI reconstruction. We hope our approach can serve as a reference for future research in dynamic MRI reconstruction.

Missing data interpolation in well logs based on generative adversarial network and improved krill herd algorithm

Accurate logging data is crucial in geology and petroleum engineering for tasks such as geological modelling, reservoir simulation, and decision-making regarding well repair, water injection, and oil recovery. However, logging instrument failure occurs due to complex conditions such as high temperature and pressure, resulting in incomplete data and posing challenges for reservoir evaluation and development. The existing interpolation methods are primarily based on statistical and machine learning methods, lacking deep mining of hidden associations between logging items. Aiming at the problem of incomplete well-logging data, an incomplete well-logging data interpolation method based on a generative adversarial network and an improved krill herd algorithm is proposed. The results show that the proposed method has stable interpolation for well-logging data missing with different missing rates and any missing positions. Compared with other GANs (GAN, WGAN, and WGAN-GP), the RMSE of the proposed method is reduced by 57.63%, and the R2 is increased by 7.94%. The proposed method is compared with statistical methods (averaging and cubic spline interpolation) and machine learning methods (k-nearest neighbor, support vector machine, and random forest). The experimental results show that the proposed model has stable reconstruction performance for logging data with different missing rates and any missing positions.

A Novel RPCA Method Using Log-Weighted Nuclear and L_(2,1) Norms Combined with Contrast-Limited Adaptive Histogram Equalization (CLAHE) for High Dimensional Natural and Medical Image Data

Estimating the true underlying images from distorted high-dimensional data is crucial for applications in high-profile fields such as crime detection in security, clinical settings and medical diagnosis in healthcare, and radar imaging in signal processing. Existing statistical methods often struggle with robustness and image reconstruction quality when processing high-dimensional image data. While Robust Principal Component Analysis (RPCA) is widely used for image recovery, its reliance on uniform weights with singular value decomposition (SVD) weakens performance, especially in noisy environments. The L1 norm also fails to capture image details and recovery under high noise levels, a critical limitation for applications like medical diagnoses, where detail is essential. These challenges emphasize the need for improved methods to handle noise and enhance image quality in sensitive fields. Therefore, this paper proposes a novel RPCA method that integrates CLAHE with Log weighted nuclear norm (LWNN) and the L2,1 norm for high-dimensional natural and medical imaging. To reduce the computational load, our novel method is formulated into a new optimization problem and solved using the Alternating Direction Method of Multipliers (ADMM). This method leverages LWNN for enhanced low-rank approximation to drastically prune out the anomalies in images and the norm for improved sparse component recovery. Our approach has superior performance in image reconstruction compared to other state-of-the-art methods (SOTAs), showing significant advancements with real-world datasets. An interesting finding of this research is that combining the LWNN with the L2,1 norm is highly effective at removing noise from images. Furthermore, when the CLAHE technique is combined with LWNN and the L2,1 norm, it significantly enhances the extraction of previously unseen features, making blood vessels in medical images much clearer and more distinguishable. This combination proves to be a powerful approach for medical image analysis, revealing details that are otherwise difficult to detect. This method will be used for crime detection in security intelligence, and clinical settings and medical diagnosis in human retinal eyes.

Adding Noise to Super-Resolution Training Set: Method to Denoise Super Resolution for Structure from Motion Preprocessing

The resolution and noise levels of input images directly affect the three-dimensional (3D) structure-from-motion (SfM) reconstruction performance. Conventional super-resolution (SR) methods focus too little on denoising, and latent image noise becomes worse when resolution is improved. This study proposes two SR denoising training algorithms to simultaneously improve resolution and noise: add-noise-before-downsampling and downsample-before-adding-noise. These portable methods preprocess low-resolution training images using real-world noise samples instead of altering the basic neural network. Hence, they concurrently improve resolution while reducing noise for an overall cleaner SfM performance. We applied these methods to the existing SR network: super-resolution convolutional neural network, enhanced deep residual super-resolution, residual channel attention network, and efficient super-resolution transformer, comparing their performances with those of conventional methods. Impressive peak signal-to-noise and structural similarity improvements of 0.12 dB and 0.56 were achieved on the noisy images of Smartphone Image Denoising Dataset, respectively, without altering the network structure. The proposed methods caused a very small loss (<0.01 dB) on clean images. Moreover, using the proposed SR algorithm makes the 3D SfM reconstruction more complete. Upon applying the methods to non-preprocessed and conventionally preprocessed models, the mean projection error was reduced by a maximum of 27% and 4%, respectively, and the number of 3D densified points was improved by 310% and 7%, respectively.

VAEEG: Variational Auto-encoder for Extracting EEG Representation

The electroencephalogram (EEG) exhibits characteristics of complexity and strong randomness. Existing deep learning models for EEG typically target specific objectives and datasets, with their scalability constrained by the size of the dataset, resulting in limited perceptual and generalization abilities. In order to obtain more intuitive, concise, and useful representations of brain activity, we constructed a reconstruction-based self-supervised learning model for EEG based on Variational Autoencoder (VAE) with separate frequency bands, termed variational auto-encoder for EEG (VAEEG). VAEEG achieved outstanding reconstruction performance. Furthermore, we validated the efficacy of the latent representations in three clinical tasks concerning pediatric brain development, epileptic seizure, and sleep stage classification. We discovered that certain latent features: 1) correlate with adolescent brain developmental changes; 2) exhibit significant distinctions in the distribution between epileptic seizures and background activity; 3) show significant variations across different sleep cycles. In corresponding downstream fitting or classification tasks, models constructed based on the representations extracted by VAEEG demonstrated superior performance. Our model can extract effective features from complex EEG signals, serving as an early feature extractor for downstream classification tasks. This reduces the amount of data required for downstream tasks, simplifies the complexity of downstream models, and streamlines the training process.

Optimizing Parameters for Enhanced Iterative Image Reconstruction Using Extended Power Divergence

In this paper, we propose a method for optimizing the parameter values in iterative reconstruction algorithms that include adjustable parameters in order to optimize the reconstruction performance. Specifically, we focus on the power divergence-based expectation-maximization algorithm, which includes two power indices as adjustable parameters. Through numerical and physical experiments, we demonstrate that optimizing the evaluation function based on the extended power-divergence and weighted extended power-divergence measures yields high-quality image reconstruction. Notably, the optimal parameter values derived from the proposed method produce reconstruction results comparable to those obtained using the true image, even when using distance functions based on differences between forward projection data and measured projection data, as verified by numerical experiments. These results suggest that the proposed method effectively improves reconstruction quality without the need for machine-learning techniques in parameter selection. Our findings also indicate that this approach is useful for enhancing the performance of iterative reconstruction algorithms, especially in medical imaging, where high-accuracy reconstruction under noisy conditions is required.

A data assimilation pressure field measurement method for linear turbine cascades based on compressed sensing

One of the most dominant measuring techniques for linear turbine cascades is to obtain the high spatial resolution pressure field by a discrete point array. In this work, a compressed sensing (CS) based data assimilation methodology is proposed, by which a set of optimal sparse measuring points can be derived through an optimization procedure. Combined with numerical simulations and the data-driven modal decomposition, the high spatial resolution pressure distribution can be reconstructed accurately with sparse random sampling. To this end, detailed comparative research is conducted. First, the impacts of different sparse bases, including the discrete Fourier transform (DFT), the discrete cosine transform (DCT), and the proper orthogonal decomposition (POD) matrices, and the compression ratio on the reconstruction performance are compared and analyzed systematically under different incidence angles and cascade exit isentropic Mach numbers. Results reveal that a CS approach on POD subspace (CS-POD) performs remarkably better than the CS-DFT and CS-DCT in capturing the spatially continuous pressure distribution, even with a small number of measuring points. Furthermore, effects of the order of truncated POD modes and the number of training dataset required to conduct POD on the error are also investigated that exhibits a downward trend with the rise in these two elements. To overcome the deficiency of randomly selected sparse observation sites with this methodology and the resulting high measuring cost under different conditions, a vectorized CS-POD (Vec-CS-POD) model is constructed to obtain one set of measuring distribution that could satisfy the multi-conditional measurements simultaneously, and its reliability and robustness are validated through cascade experiments. With the aid of the Vec-CS-POD based data fusion framework, spatially resolved end wall pressure fields can be acquired by only a few measuring ports, the number of which can be reduced by 77% compared to the conventional uniform arrangement. The generalization ability of the proposed framework is also validated and evaluated; thus, it exhibits broad potential in other flow field measurements.

Disentangled feature fusion network for lightweight image super-resolution

Recently, the quality of generated images in image super-resolution (SR) has significantly improved due to the widespread application of convolutional neural networks. Existing super-resolution methods often overlook the importance of network width and instead prioritize designing deeper network structures to improve performance. Deeper networks, however, demand greater computational resources and are more challenging to train, making them less suitable for devices with limited performance. To address this issue, we propose a novel method called the disentangled feature fusion network (DFFN). Specifically, we construct dual feature extraction streams (DFES) using the strategies of feature disentanglement and parameter sharing, which increase the network's width without increasing the number of parameters. To facilitate the interaction of information between the dual feature extraction streams, we design a feature interaction module (FIM) that separately enhances high and low-frequency features. Furthermore, a feature fusion module (FFM) is presented to efficiently fuse different levels of high and low-frequency feature information. The proposed DFFN integrates DFES, FIM, and FFM, which not only widens the network without increasing parameters but also minimizes the loss of crucial feature information. Extensive experiments on five benchmark datasets demonstrate that our method significantly outperforms other state-of-the-art lightweight super-resolution methods, achieving a superior balance between parameter quantity, reconstruction performance, and model complexity. The code is available at https://github.com/zhoujy-aust/DFFN.

Image reconstruction from compressed measurements for ultrasound NDT

In ultrasound NDT, Delay-and-Sum schemes are commonly used to reconstruct images from the measurement data. For single channel pulse-echo measurements, this is called Synthetic Aperture Focusing Technique (SAFT). In a multi-channel setup, SAFT is extended to the Total Focusing Method (TFM), where the focused image is reconstructed from measurements of all transmit-receive combinations, called Full Matrix Capture (FMC). In previous work, we showed that both SAFT and TFM can be cast as a sparse recovery problem that enables enhancing the Delay-and-Sum scheme to a physically motivated forward model that leads to improved image quality. The reconstruction is performed using l1 minimization. Further, this also allows to perform the reconstruction from compressed/subsampled measurements following compressed sensing theory. This subsampling was achieved by only measuring a subset of the frequency coefficients of the signal. In both the single channel and the multi-channel setup the amount of measurement data is thereby significantly reduced. Additionally, for the TFM, we added a spatial subsampling by only considering a subset of transmit and receive pairs, further reducing the measurement data and at the same time also reducing the measurement time. In our previous work, the frequency and spatial dimensions were considered separately for simplicity, using the same compression strategy for all spatial measurements. In this work we consider joint frequency/spatial compression schemes. We show that a joint multidimensional compression strategy where different Fourier subsets are considered for each channel pair leads to a superior reconstruction performance when the compression rate is fixed. Analysis based on an analytical model for a single defect and a real-world example with multiple defects shows that the approach works in practice.

A novel recursive sub-tensor hyperspectral compressive sensing of plant leaves based on multiple arbitrary-shape regions of interest

Plant hyperspectral images (HSIs) contain valuable information for agricultural disaster prediction, biomass estimation, and other applications. However, they also include a lot of irrelevant background information, which wastes storage resources. In this paper, we propose a novel recursive sub-tensor hyperspectral compressive sensing method for plant leaves. This method uses recursive sub-tensor compressive sensing to compress and reconstruct each arbitrary-shape leaf region, discarding a large amount of background information to achieve the best possible reconstruction performance of the leaf region and significantly reduce storage space. The proposed method involves several key steps. Firstly, the optimal band is determined using the spatial spectral decorrelation criterion, and its corresponding mask image is used to extract the leaf regions from the background. Secondly, the recursive maximum inscribed rectangle algorithm is applied to obtain rectangular sub-tensors of leaves recursively. Each sub-tensor is then individually compressed and reconstructed. Finally, all sub-tensors can be reconstructed to form complete leaf HSIs without background information. Experimental results demonstrate that the proposed method achieves superior image reconstruction quality at extremely low sampling rates compared to other methods. The proposed method can improve average Peak Signal-to-Noise Ratio (PSNR) values by about 3.04% and 0.74% compared to Tensor Compressive Sensing (TCS) at the sampling rate of 2%. In the spectral domain, the proposed method can achieve significantly smaller Spectral Angle Mapper (SAM) values and relatively lower spectral indices errors for Double Difference, Triangular Vegetation Index, Leaf Chlorophyll Index, and Modified Normalized Difference 680 than those of TCS. Therefore, the proposed method achieves better compression performance for reconstructed plant leaf HSIs than the other methods.

Enhanced Multi-Scale Attention-Driven 3D Human Reconstruction from Single Image

Due to the inherent limitations of a single viewpoint, reconstructing 3D human meshes from a single image has long been a challenging task. While deep learning networks enable us to approximate the shape of unseen sides, capturing the texture details of the non-visible side remains difficult with just one image. Traditional methods utilize Generative Adversarial Networks (GANs) to predict the normal maps of the non-visible side, thereby inferring detailed textures and wrinkles on the model’s surface. However, we have identified challenges with existing normal prediction networks when dealing with complex scenes, such as a lack of focus on local features and insufficient modeling of spatial relationships.To address these challenges, we introduce EMAR—Enhanced Multi-scale Attention-Driven Single-Image 3D Human Reconstruction. This approach incorporates a novel Enhanced Multi-Scale Attention (EMSA) mechanism, which excels at capturing intricate features and global relationships in complex scenes. EMSA surpasses traditional single-scale attention mechanisms by adaptively adjusting the weights between features, enabling the network to more effectively leverage information across various scales. Furthermore, we have improved the feature fusion method to better integrate representations from different scales. This enhanced feature fusion allows the network to more comprehensively understand both fine details and global structures within the image. Finally, we have designed a hybrid loss function tailored to the introduced attention mechanism and feature fusion method, optimizing the network’s training process and enhancing the quality of reconstruction results. Our network demonstrates significant improvements in performance for 3D human model reconstruction. Experimental results show that our method exhibits greater robustness to challenging poses compared to traditional single-scale approaches.

Alveolar Bone Reconstruction Simultaneous to Implant Removal due to Advanced Peri-Implantitis Defects: A Proof of Concept.

To evaluate the safety and effectiveness of alveolar bone reconstruction simultaneous to implant removal due to peri-implantitis. Partial or fully dentulous patients subjected to implant removal due to advanced peri-implantitis (≥ 50% of bone loss) lesions and seeking to have the failed implant replaced for esthetic or functional reasons were consecutively included. Guided bone regeneration was performed by means of a mixture of xenograft and autogenous bone and a ribose cross-linked barrier membrane. Re-entry for implant placement was performed at 4-month follow-up. Overall, six radiographic variables were assessed before (T0) and after (T1) alveolar bone reconstruction at four levels in ridge width (RW) and height (RH). Peri-implant conditions were evaluated at latest follow-up. Simple and multiple binary logistic regression models were calculated using generalized estimation equations to evaluate the effect of baseline upon reconstructive outcomes. In total, 20 patients (nimplant = 39) met the inclusion criteria. Alveolar RW and RH were augmented from T0 to T1 at all levels. All implants achieved primary stability. Only ~13% were subjected to ancillary bone regeneration simultaneous to implant placement. After a mean follow-up period after loading of ~2.2 years, ~70% implants demonstrated peri-implant health, while mucositis was diagnosed in the remaining implants. The performance of alveolar bone reconstruction in residual partially contained defects simultaneous to implant removal due to peri-implantitis lesions demonstrates being safe and effective for implant site development.

Description Generation Using Variational Auto-Encoders for Precursor microRNA.

Micro RNAs (miRNA) are a type of non-coding RNA involved in gene regulation and can be associated with diseases such as cancer, cardiovascular, and neurological diseases. As such, identifying the entire genome of miRNA can be of great relevance. Since experimental methods for novel precursor miRNA (pre-miRNA) detection are complex and expensive, computational detection using Machine Learning (ML) could be useful. Existing ML methods are often complex black boxes that do not create an interpretable structural description of pre-miRNA. In this paper, we propose a novel framework that makes use of generative modeling through Variational Auto-Encoders to uncover the generative factors of pre-miRNA. After training the VAE, the pre-miRNA description is developed using a decision tree on the lower dimensional latent space. Applying the framework to miRNA classification, we obtain a high reconstruction and classification performance while also developing an accurate miRNA description.

Dark energy reconstruction analysis with artificial neural networks: Application on simulated Supernova Ia data from Rubin Observatory

In this paper, we present an analysis of Supernova Ia (SNIa) distance moduli (μ(z)) and dark energy using an Artificial Neural Network (ANN) reconstruction based on LSST simulated three-year SNIa data. The ANNs employed in this study utilize genetic algorithms for hyperparameter tuning and Monte Carlo Dropout for predictions. Our ANN reconstruction architecture is capable of modeling both the distance moduli and their associated statistical errors given redshift values. We compare the performance of the ANN-based reconstruction with two theoretical dark energy models: ΛCDM and Chevallier–Linder–Polarski (CPL). Bayesian analysis is conducted for these theoretical models using the LSST simulations and compared with observations from Pantheon and Pantheon+ SNIa real data. We demonstrate that our model-independent ANN reconstruction is consistent with both theoretical models. Performance metrics and statistical tests reveal that the ANN produces distance modulus estimates that align well with the LSST dataset and exhibit only minor discrepancies with ΛCDM and CPL.