Xiaoli Wang

Objective . The study aims to tackle optical aberrations induced by sample non-uniformity or imaging system deviations in Fourier Ptychographic Microscopy (FPM). This technique enables high-resolution microscopic imaging with a wide field of view. The proposed approach involves employing a multi-feature fusion residual network method for aberration correction. This method leverages residual networks and multi-scale convolutional kernels to extract deep residual features and enhance the capacity to capture pertinent image characteristics. Approach . This method optimizes network performance by utilizing grouped convolutions and attention mechanisms without significantly increasing the parameter count. Through the combination of Zernike modes, the accurate estimation and correction of aberrations are achieved, resulting in a substantial enhancement in imaging quality. Main results . The research shows the effective correction of aberrations using this method, facilitating high-resolution image reconstruction while preserving intricate texture details. Validation results, derived from simulated and real images, highlight the efficacy of the method. Significance . By introducing the multi-feature fusion residual network method, this study addresses a significant challenge posed by aberrations in FPM, enhancing imaging quality and presenting a new effective approach for high-resolution microscopic imaging. These results have important implications for fields such as biomedicine, which require stringent image quality standards.

Fourier ptychographic microscopy (FPM) achieves high-resolution reconstruction by stitching multi-angle illuminated images in the frequency domain, but its traditional full sampling mode requires activating a large number of LED sources, leading to data redundancy and excessive time costs. To address these issues, this paper proposes an illumination optimization strategy based on regional energy sensitivity (RES) and LED array sub-region division, termed RES-FPM, to improve the imaging efficiency of FPM. The method first constructs a frequency-domain sampling window based on the initial high-resolution spectrum map and generates the spatial response map corresponding to each LED through inverse Fourier transform. It then calculates the degree of influence of each LED on the image’s regional energy (RES score). To avoid spatial non-uniformity caused by direct global ranking-based LED selection, this work further divides the LED array into spatial sub-regions. Within each sub-region, several representative LEDs are selected based on their local energy contribution, achieving a spatial-energy-balanced co-optimization. Experimental results demonstrate that, while maintaining spectral coverage and image reconstruction quality, RES-FPM reduces the required image acquisitions by ∼54.4%, yet preserves reconstruction performance comparable to traditional FPM sampling. This significantly lowers computational costs for both data acquisition and reconstruction while improving efficiency. Additionally, it exhibits strong robustness and interpretability when dealing with complex sample structural variations.

Breast cancer is one of the most prevalent malignant tumors among women worldwide, underscoring the urgent need for early and accurate diagnosis to reduce mortality. To address this, A Multi-Feature Fusion Classification Network (MFF-ClassificationNet) is proposed for breast histopathological image classification. The network adopts a two-branch parallel architecture, where a convolutional neural network captures local details and a Transformer models global dependencies. Their features are deeply integrated through a Multi-Feature Fusion module, which incorporates a Convolutional Block Attention Module—Squeeze and Excitation (CBAM-SE) fusion block combining convolutional block attention, squeeze-and-excitation mechanisms, and a residual inverted multilayer perceptron to enhance fine-grained feature representation and category-specific lesion characterization. Experimental evaluations on the BreakHis dataset achieved accuracies of 98.30%, 97.62%, 98.81%, and 96.07% at magnifications of 40×, 100×, 200×, and 400×, respectively, while an accuracy of 97.50% was obtained on the BACH dataset. These results confirm that integrating local and global features significantly strengthens the model’s ability to capture multi-scale and context-aware information, leading to superior classification performance. Overall, MFF-ClassificationNet surpasses conventional single-path approaches and provides a robust, generalizable framework for advancing computer-aided diagnosis of breast cancer.

Vehicle detection in remote sensing images faces significant challenges due to small object sizes, scale variation, and cluttered backgrounds. To address these issues, we propose RSW-YOLO, an enhanced detection model built upon the YOLOv8n framework, designed to improve feature extraction and robustness against environmental noise. A Restormer module is incorporated into the backbone to model long-range dependencies via self-attention, enabling better handling of multi-scale features and complex scenes. A dedicated detection head is introduced for small objects, focusing on critical channels while suppressing irrelevant information. Additionally, the original CIoU loss is replaced with WIoU, which dynamically reweights predicted boxes based on their quality, enhancing localization accuracy and stability. Experimental results on the DJCAR dataset show mAP@0.5 and mAP@0.5:0.95 improvements of 5.4% and 6.2%, respectively, and corresponding gains of 4.3% and 2.6% on the VisDrone dataset. These results demonstrate that RSW-YOLO offers a robust and accurate solution for UAV-based vehicle detection, particularly in urban scenes with dense or small targets.

This paper proposes an optimized algorithm based on YOLOv11s to address the problem of insufficient detection accuracy of vehicle targets from a drone perspective due to certain scenes involving complex backgrounds, dense vehicle targets, and/or large variations in vehicle target scales due to oblique imaging. The proposed algorithm enhances the model’s local feature extraction capability through a module collaboration optimization strategy, integrates coordinate convolution to strengthen spatial perception, and introduces a small object detection head to address target size variations caused by altitude changes. Additionally, we construct a dedicated dataset for urban vehicle detection that is characterized by high-resolution images, a large sample size, and low training resource requirements. Experimental results show that the proposed algorithm achieves gains of 1.9% in precision, 6.0% in recall, 4.2% in mAP@0.5, and 3.3% in mAP@0.5:0.95 compared to the baseline network. The improved model also achieves the highest F1-score, indicating an optimal balance between precision and recall.

Autonomous Fourier Ptychographic Microscopy (FPM) is a technology widely used in the field of pathology. It is compatible with high resolution and large field-of-view imaging and can observe more image details. Red blood cells play an indispensable role in assessing the oxygen-carrying capacity of the human body and in screening for clinical diagnosis and treatment needs. In this paper, the blood cell data set is constructed based on the FPM system experimental platform. Before training, four enhancement strategies are adopted for the blood cell image data to improve the generalization and robustness of the model. A blood cell detection algorithm based on SCD-YOLOv7 is proposed. Firstly, the C-MP (Convolutional Max Pooling) module and DELAN (Deep Efficient Learning Automotive Network) module are used in the feature extraction network to optimize the feature extraction process and improve the extraction ability of overlapping cell features by considering the characteristics of channels and spatial dimensions. Secondly, through the Sim-Head detection head, the global information of the deep feature map (mean average precision) and the local details of the shallow feature map are fully utilized to improve the performance of the algorithm for small target detection. MAP is a comprehensive indicator for evaluating the performance of object detection algorithms, which measures the accuracy and robustness of a model by calculating the average precision (AP) under different categories or thresholds. Finally, the Focal-EIoU (Focal Extended Intersection over Union) loss function is introduced, which not only improves the convergence speed of the model but also significantly improves the accuracy of blood cell detection. Through quantitative and qualitative analysis of ablation experiments and comparative experimental results, the detection accuracy of the SCD-YOLOv7 algorithm on the blood cell data set reached 92.4%, increased by 7.2%, and the calculation amount was reduced by 14.6 G.

Fourier Ptychographic Microscopy (FPM) is a microscopy imaging technique based on optical principles. It employs Fourier optics to separate and combine different optical information from a sample. However, noise introduced during the imaging process often results in poor resolution of the reconstructed image. This article has designed an approach based on a residual local mixture network to improve the quality of Fourier ptychographic reconstruction images. By incorporating channel attention and spatial attention into the FPM reconstruction process, the network enhances the efficiency of the network reconstruction and reduces the reconstruction time. Additionally, the introduction of the Gaussian diffusion model further reduces coherent artifacts and improves image reconstruction quality. Comparative experimental results indicate that this network achieves better reconstruction quality, and outperforming existing methods in both subjective observation and objective quantitative evaluation.

Fourier ptychographic microscopy, as a computational imaging method, can reconstruct high-resolution images but suffers optical aberration, which affects its imaging quality. For this reason, this paper proposes a network model for simulating the forward imaging process in the Tensorflow framework using samples and coherent transfer functions as the input. The proposed model improves the introduced Wirtinger flow algorithm, retains the central idea, simplifies the calculation process, and optimizes the update through back propagation. In addition, Zernike polynomials are used to accurately estimate aberration. The simulation and experimental results show that this method can effectively improve the accuracy of aberration correction, maintain good correction performance under complex scenes, and reduce the influence of optical aberration on imaging quality.

Fourier ptychographic microscopy (FPM) is a computational imaging technology that has endless vitality and application potential in digital pathology. Colored pathological image analysis is the foundation of clinical diagnosis, basic research, and most biomedical problems. However, the current colorful FPM reconstruction methods are time-inefficient, resulting in poor image quality due to optical interference and reconstruction errors. This paper combines coloring and FPM to propose a self-supervised generative adversarial network (GAN) for FPM color reconstruction. We design a generator based on the efficient channel residual (ECR) block to adaptively obtain efficient cross-channel interaction information in a lightweight manner, and we introduce content-consistency loss to learn the high-frequency information of the image and improve the image quality of the staining. Furthermore, the effectiveness of our proposed method is demonstrated through objective indicators and visual evaluations.

The cornea is an important refractive structure in the human eye. The corneal segmentation technique provides valuable information for clinical diagnoses, such as corneal thickness. Non-contact anterior segment optical coherence tomography (AS-OCT) is a prevalent ophthalmic imaging technique that can visualize the anterior and posterior surfaces of the cornea. Nonetheless, during the imaging process, saturation artifacts are commonly generated due to the tangent of the corneal surface at that point, which is normal to the incident light source. This stripe-shaped saturation artifact covers the corneal surface, causing blurring of the corneal edge, reducing the accuracy of corneal segmentation. To settle this matter, an inpainting method that introduces structural similarity and frequency loss is proposed to remove the saturation artifact in AS-OCT images. Specifically, the structural similarity loss reconstructs the corneal structure and restores corneal textural details. The frequency loss combines the spatial domain with the frequency domain to ensure the overall consistency of the image in both domains. Furthermore, the performance of the proposed method in corneal segmentation tasks is evaluated, and the results indicate a significant benefit for subsequent clinical analysis.