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Background Advancements in single-cell RNA sequencing have enabled the analysis of millions of cells, but integrating such data across samples and methods while mitigating batch effects remains challenging. Deep learning approaches address this by learning biologically conserved gene expression representations, yet systematic benchmarking of loss functions and integration performance is lacking. Results We evaluate 16 integration methods using a unified variational autoencoder framework, incorporating batch and cell-type information. Results reveal limitations in the single-cell integration benchmarking index (scIB) for preserving intra-cell-type information. To address this, we introduce a correlation-based loss function and enhance benchmarking metrics to better capture biological conservation. Using cell annotations from lung and breast atlases, our approach improves biological signal preservation. We propose a refined integration framework, scIB-E, and metrics that provide deeper insights into the integration process and offer guidance for advanced developments in integrating increasingly complex single-cell data. Conclusions This benchmark highlights the potential of deep learning-based approaches for single-cell data integration, emphasizing the importance of biologically informed metrics and improved benchmarking strategies.

Anomaly detection (AD) is an important topic in remote sensing, aiming to identify unusual or abnormal features within the data. However, most existing low-rank representation methods usually use the nuclear norm for background estimation, and do not consider the different contributions of different singular values. Besides, they overlook the spatial relationships of abnormal regions, particularly failing to fully leverage the 3D structured information of the data. Moreover, noise in practical scenarios can disrupt the low-rank structure of the background, making it challenging to separate anomaly from the background and ultimately reducing detection accuracy. To address these challenges, this paper proposes a weighted multidirectional sparsity regularized low-rank tensor representation method (WMS-LRTR) for AD. WMS-LRTR uses the weighted tensor nuclear norm for background estimation to characterize the low-rank property of the background. Considering the correlation between abnormal pixels across different dimensions, the proposed method introduces a novel weighted multidirectional sparsity (WMS) by unfolding anomaly into multimodal to better exploit the sparsity of the anomaly. In order to improve the robustness of AD, we further embed a user-friendly plug-and-play (PnP) denoising prior to optimize the background modeling under low-rank structure and facilitate the separation of sparse anomalous regions. Furthermore, an effective iterative algorithm using alternate direction method of multipliers (ADMM) is introduced, whose subproblems can be solved quickly by fast solvers or have closed-form solutions. Numerical experiments on various datasets show that WMS-LRTR outperforms state-of-the-art AD methods, demonstrating its better detection ability.

Accurate image registration is essential for various remote sensing applications, particularly in multi-temporal image analysis. This paper introduces DVF-NET, a novel deep learning-based framework for dual-temporal remote sensing image registration. DVF-NET integrates two displacement vector fields to address nonlinear distortions caused by significant variations between images, enabling more precise image alignment. A key innovation of this method is the incorporation of a Structural Attention Module (SAT), which enhances the model’s ability to focus on structural features, improving the feature extraction process. Additionally, we propose a novel loss function design that combines multiple similarity metrics, ensuring more comprehensive supervision during training. Experimental results on various remote sensing datasets indicate that the proposed DVF-NET outperforms the existing methods in both accuracy and robustness, particularly when handling images with substantial geometric distortions such as tilted buildings. The results validate the effectiveness of our approach and highlight its potential for various remote sensing tasks, including change detection, land cover classification, and environmental monitoring. DVF-NET provides a promising direction for the advancement of remote sensing image registration techniques, offering both high precision and robustness in complex real-world scenarios.

Quantum watermarking is a technique that embeds specific information into a quantum carrier for the purpose of digital copyright protection. In this paper, we propose a novel color image watermarking algorithm that integrates quantum discrete wavelet transform with Sinusoidal–Tent mapping and baker mapping. Initially, chaotic sequences are generated using Sinusoidal–Tent mapping to determine the channels suitable for watermark embedding. Subsequently, a one-level quantum Haar wavelet transform is applied to the selected channel to decompose the image. The watermarked image is then scrambled via discrete baker mapping, and the scrambled image is embedded into the High-High subbands. The invisibility of the watermark is evaluated by calculating the peak signal-to-noise ratio, Structural similarity index measure, and Learned Perceptual Image Patch Similarity, with comparisons made against the color histogram. The robustness of the proposed algorithm is assessed through the calculation of Normalized Cross-Correlation. In the simulation results, PSNR is close to 63, SSIM is close to 1, LPIPS is close to 0.001, and NCC is close to 0.97. This indicates that the proposed watermarking algorithm exhibits excellent visual quality and a robust capability to withstand various attacks. Additionally, through ablation study, the contribution of each technique to overall performance was systematically evaluated.

Over the past few decades, data science and machine learning have demonstrated tremendous success in many areas of science and engineering, such as large-scale pattern recognition, computer vision, multiagent control, industrial engineering, etc [...]

Visual navigation based on image matching has become one of the most important research fields for UAVs to achieve autonomous navigation, because of its low cost, strong anti-jamming ability, and high performance. Currently, numerous positioning and navigation methods based on visual information have been proposed for UAV navigation. However, the appearance, shape, color, and texture of objects can change significantly due to different lighting conditions, shadows, and surface coverage during different seasons, such as vegetation cover in summer or ice and snow cover in winter. These changes pose greater challenges for feature-based image matching methods. This encouraged us to overcome the limitations of previous works, which did not consider significant seasonal changes such as snow-covered UAV aerial images, by proposing an image matching method using season-changing UAV aerial images and satellite imagery. Following the pipeline of a two-channel deep convolutional neural network, we first pre-scaled the UAV aerial images, ensuring that the UAV aerial images and satellite imagery had the same ground sampling distance. Then, we introduced attention mechanisms to provide additional supervision for both low-level local features and high-level global features, resulting in a new season-specific feature representation. The similarity between image patches was calculated using a similarity measurement layer composed of two fully connected layers. Subsequently, we conducted template matching to estimate the UAV matching position with the highest similarity. Finally, we validated our proposed method on both synthetic and real UAV aerial image datasets, and conducted direct comparisons with previous popular works. The experimental results demonstrated that our method achieved the highest matching accuracy on multi-temporal and multi-season images.

Euler’s elastica-based unsupervised segmentation models have strong capability of completing the missing boundaries for existing objects in a clean image, but they are not working well for noisy images. This paper aims to establish a Euler’s elastica-based approach that can properly deal with the random noises to improve the segmentation performance for noisy images. The corresponding formulation of stochastic optimization is solved via the progressive hedging algorithm (PHA), and the description of each individual scenario is obtained by the alternating direction method of multipliers. Technically, all the sub-problems derived from the framework of PHA can be solved by using the curvature-weighted approach and the convex relaxation method. Then, an alternating optimization strategy is applied by using some powerful accelerating techniques including the fast Fourier transform and generalized soft threshold formulas. Extensive experiments have been conducted on both synthetic and real images, which displayed significant gains of the proposed segmentation models and demonstrated the advantages of the developed algorithms.

The classical non-local Total Variation model has been extensively used for gray texture image inpainting previously, but such model can not be directly applied to color texture image inpainting due to coupling of different image channels in color images. In order to solve the inpainting problem for color texture images effectively, we propose a non-local Color Total Variation model. This model is different from the recently proposed non-local Mumford–Shah model (NL-MS). Technically, the proposed model is an extension of local TV model for gray images but we take account of the relationship between different channels in color images and make use of concepts of the non-local operators. We will analyze how the coupling of different channels of color images in the proposed model makes the problem difficult for numerical implementation with the conventional split Bregman algorithm. In order to solve the proposed model efficiently, we propose a fast heuristic numerical algorithm based on the split Bregman algorithm with introduction of a threshold function. The performance of the proposed model with the proposed heuristic algorithm is compared with the NL-MS model. Extensive numerical experiments have shown that the proposed model and algorithm have superior excellent performance as well as with much faster speed.

Diffusion is a commonly used technique for spreading information from point to point on a graph. The rationale behind diffusion is not clear. The multi-type Galton–Watson forest is a random model of population growth without space or any other resource constraints. In this paper, we use the degenerated multi-type Galton–Watson forest (MGWF) to interpret the diffusion process, corresponding vertices to types and establishing an equivalence relationship between them. With the two-phase setting of the MGWF, one can interpret the diffusion process and the Google PageRank system explicitly. It also improves the convergence behavior of the iterative diffusion process and Google PageRank system. We validate the proposal by experiment while providing new research directions.

Preserving contour topology during image segmentation is useful in many practical scenarios. By keeping the contours isomorphic, it is possible to prevent over-segmentation and under-segmentation, as well as to adhere to given topologies. The Self-repelling Snakes model (SR) is a variational model that preserves contour topology by combining a non-local repulsion term with the geodesic active contour model. The SR is traditionally solved using the additive operator splitting (AOS) scheme. In our paper, we propose an alternative solution to the SR using the Split Bregman method. Our algorithm breaks the problem down into simpler sub-problems to use lower-order evolution equations and a simple projection scheme rather than re-initialization. The sub-problems can be solved via fast Fourier transform or an approximate soft thresholding formula which maintains stability, shortening the convergence time, and reduces the memory requirement. The Split Bregman and AOS algorithms are compared theoretically and experimentally.