In the context of mountain tunnel mining through the drilling and blasting method, the recognition of lithology from palm face images is crucial for the comprehensive analysis of geological conditions and the prevention of geological risks. However, the complexity of the background in the acquired palm face images, coupled with an insufficient data sample size, poses challenges. While the incorporation of deep learning technology has enhanced lithology recognition accuracy, issues persist, including inadequate feature extraction and suboptimal recognition accuracy. To address these challenges, this paper proposes a lithology recognition network integrating attention mechanisms and a feature Brownian distance covariance approach. Drawing inspiration from the Brownian distance covariance concept, a feature Brownian distance covariance module is devised to enhance the network’s attention to rock sample features and improve classification accuracy. Furthermore, an enhanced lightweight Convolutional Block Attention Module is introduced, with upgrades to the multilayer perceptron in the channel attention module. These improvements emphasize attention to lithological features while mitigating interference from background information. The proposed method is evaluated on a palm face image dataset collected in the field. The proposed method was evaluated on a dataset comprising field-collected images of a tunnel rock face. The results illustrate a significant enhancement in the improved model’s ability to recognize rock images, as evidenced by improvements across all objective evaluation metrics. The achieved accuracy rate of 97.60% surpasses that of the current mainstream lithology recognition neural network.

An adaptive optics scanning laser ophthalmoscope (AOSLO) has the characteristics of a high resolution and a small field of view (FOV), which are greatly affected by eye motion. Continual eye motion will cause distortions both within the frame (intra-frame) and between frames (inter-frame). Overcoming eye motion and achieving image stabilization is the first step and is of great importance in image analysis. Although cross-correlation-based methods enable image registration to be achieved, the manual identification and distinguishing of images with saccades is required; manual registration has a high accuracy, but it is time-consuming and complicated. Some imaging systems are able to compensate for eye motion during the imaging process, but special hardware devices need to be integrated into the system. In this paper, we proposed a deep-learning-based algorithm for automatic image stabilization. The algorithm used the VGG-16 network to extract convolution features and a correlation filter to detect the position of reference in the next frame, and finally, it compensated for displacement to achieve registration. According to the results, the mean difference in the vertical and horizontal displacement between the algorithm and manual registration was 0.07 pixels and 0.16 pixels, respectively, with a 95% confidence interval of (−3.26 px, 3.40 px) and (−4.99 px, 5.30 px). The Pearson correlation coefficients for the vertical and horizontal displacements between these two methods were 0.99 and 0.99, respectively. Compared with cross-correlation-based methods, the algorithm had a higher accuracy, automatically removed images with blinks, and corrected images with saccades. Compared with manual registration, the algorithm enabled manual registration accuracy to be achieved without manual intervention.

Vessel segmentation in optical coherence tomography angiography (OCTA) is crucial for the detection and diagnosis of various eye diseases. However, it is hard to distinguish intricate vessel morphology and quantify the density of blood vessels due to the large variety of vessel sizes, significant background noise, and small datasets. To this end, a retinal angiography multi-scale segmentation network, integrated with the inception and squeeze-and-excitation modules, is proposed to address the above challenges under the one-shot learning paradigm. Specifically, the inception module extends the receptive field and extracts multi-scale features effectively to handle diverse vessel sizes. Meanwhile, the squeeze-and-excitation module modifies channel weights adaptively to improve the vessel feature extraction ability in complex noise backgrounds. Furthermore, the one-shot learning paradigm is adapted to alleviate the problem of the limited number of images in existing retinal OCTA vascular datasets. Compared with the classic U²-Net, the proposed model gains improvements in the Dice coefficient, accuracy, precision, recall, and intersection over union by 3.74%, 4.72%, 8.62%, 4.87%, and 4.32% respectively. The experimental results demonstrate that the proposed one-shot learning method is an effective solution for retinal angiography image segmentation.

Fine-grained few-shot image classification is a popular research area in deep learning. The main goal is to identify subcategories within a broader category using a limited number of samples. The challenge stems from the high intra-class variability and low inter-class variability of fine-grained images, which often hamper classification performance. To overcome this, we propose a fine-grained few-shot image classification algorithm based on bidirectional feature reconstruction. This algorithm introduces a Mixed Residual Attention Block (MRA Block), combining channel attention and window-based self-attention to capture local details in images. Additionally, the Dual Reconstruction Feature Fusion (DRFF) module is designed to enhance the model’s adaptability to both inter-class and intra-class variations by integrating features of different scales across layers. Cosine similarity networks are employed for similarity measurement, enabling precise predictions. The experiments demonstrate that the proposed method achieves classification accuracies of 96.99%, 98.53%, and 89.78% on the CUB-200-2011, Stanford Cars, and Stanford Dogs datasets, respectively, confirming the method’s efficacy in fine-grained classification tasks.