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In the domain of point cloud registration, the coarse-to-fine feature matching paradigm has received significant attention due to its impressive performance. This paradigm involves a two-step process: first, the extraction of multilevel features, and subsequently, the propagation of correspondences from coarse to fine levels. However, this approach faces two notable limitations. Firstly, the use of the Dual Softmax operation may promote one-to-one correspondences between superpoints, inadvertently excluding valuable correspondences. Secondly, it is crucial to closely examine the overlapping areas between point clouds, as only correspondences within these regions decisively determine the actual transformation. Considering these issues, we propose OAAFormer to enhance correspondence quality. On the one hand, we introduce a soft matching mechanism to facilitate the propagation of potentially valuable correspondences from coarse to fine levels. On the other hand, we integrate an overlapping region detection module to minimize mismatches to the greatest extent possible. Furthermore, we introduce a region-wise attention module with linear complexity during the fine-level matching phase, designed to enhance the discriminative capabilities of the extracted features. Tests on the challenging 3DLoMatch benchmark demonstrate that our approach leads to a substantial increase of about 7% in the inlier ratio, as well as an enhancement of 2%–4% in registration recall. Finally, to accelerate the prediction process, we replace the Conventional Random Sample Consensus (RANSAC) algorithm with the selection of a limited yet representative set of high-confidence correspondences, resulting in a 100 times speedup while still maintaining comparable registration performance. [ABSTRACT FROM AUTHOR]

Targeting the issues of slow speed and inadequate precision of optimal solution calculation for multi-user detection in complex noise environments, this paper proposes a multi-user detection algorithm based on a Hybrid Cheetah Optimizer (HCO). The algorithm first optimizes the control parameters and individual update mechanism of the Cheetah Optimizer (CO) algorithm using a nonlinear strategy to improve the uniformity and discretization of the individual search range, and then dynamically introduces a differential evolutionary algorithm into the improved selection mechanism of the CO algorithm, which is utilized to fine-tune the solution space and maintain the local diversity during the fast search process. Simulation results demonstrate that this detection algorithm not only realizes fast convergence with a very low bit error rate (BER) at eight iterations but also has obvious advantages in terms of noise immunity, resistance to far and near effects, communication capacity, etc., which greatly improves the speed and accuracy of optimal position solving for multi-user detection and can achieve the purpose of accurate solving in complex environments. [ABSTRACT FROM AUTHOR]

Breast cancer (BC) is the most commonly diagnosed non‐skin cancer in women. To achieve early and accurate diagnosis, three‐dimensional (3D) cone‐beam breast computed tomography (CBBCT) and digital breast tomosynthesis (DBT) modalities are used. Importantly, the comparison of reconstruction accuracy of both CBBCT and DBT has rarely been investigated, thus constituting a research gap. This study systematically investigated the performances of the CBBCT and DBT for different reconstruction algorithms using both BR3D breast phantom and breast images. We acquired clinical breast images and scanned the BR3D phantom for additional breast images. These acquired images were used to simulate projection data using predefined CBBCT and DBT geometries. The simulated projections were reconstructed using five different reconstruction algorithms. To evaluate the reconstruction accuracy, we calculated average image quality assessment (IQA) indices, including peak signal‐to‐noise ratio (PSNR), structural similarity index (SSIM), root mean square error (RMSE), and others, across different algorithms and modalities. The pooled PSNR, SSIM, and RMSE for DBT and CBBCT images are (31.6265 ± 0.8725), (0.9353 ± 0.0077), and (0.0270 ± 0.0025) and (29.7007 ± 0.9249), (0.9136 ± 0.0130), and (0.0342 ± 0.0040), which implies that the overall IQA indices of DBT are superior to CBBCT; therefore, DBT tends to reveal more BC detectability as the diagnosis outcome would largely depend on good quality images. The results show that DBT gives an improved result for all algorithms compared to CBBCT, although further experimental trials may be needed to establish the findings fully. The findings suggest that using DBT may enhance the accuracy of BC diagnosis compared to CBBCT due to its superior image quality in clinical practice, emphasizing the importance of selecting optimal reconstruction algorithms for improved diagnostic outcomes. [ABSTRACT FROM AUTHOR]