Single image reflection removal is particularly important in improving image quality. However, existing single image reflection removal methods cannot remove reflection in a pixel-by-pixel manner, significantly reducing their effectiveness. To address this issue, we propose a pixel-wise single image reflection removal method based on reinforcement learning. Specifically, we formalize the image reflection removal process as a sequential decision-making process and remove the single image reflection pixel by pixel. We first introduce the single image reflection removal method at each time step. We then describe the reinforcement learning setup for image reflection, including state, action, reward, and agent network design. Finally, we conducted experiments on benchmark datasets, and the results show that our method outperforms other single image reflection removal methods.

Few-shot learning is a crucial approach for macromolecule classification of the cryo-electron tomography (Cryo-ET) subvolumes, enabling rapid adaptation to novel tasks with a small support set of labeled data. However, existing few-shot classification methods for macromolecules in Cryo-ET consider only marginal distributions and overlook joint distributions, failing to capture feature dependencies fully. To address this issue, we propose a method for macromolecular few-shot classification using deep Brownian Distance Covariance (BDC). Our method models the joint distribution within a transfer learning framework, enhancing the modeling capabilities. We insert the BDC module after the feature extractor and only train the feature extractor during the training phase. Then, we enhance the model's generalization capability with self-distillation techniques. In the adaptation phase, we fine-tune the classifier with minimal labeled data. We conduct experiments on publicly available SHREC datasets and a small-scale synthetic dataset to evaluate our method. Results show that our method improves the classification capabilities by introducing the joint distribution.

MOTIVATION: Cryogenic electron tomography (cryo-ET) enables high-resolution 3D reconstruction of biological samples, with accurate subtomogram classification critical for structural analysis. However, current subtomogram classification methods often struggle with out-of-distribution (OOD) data issue, causing misclassification and mismatched structures. RESULTS: To solve this problem, we propose a unified subtomogram classification framework that incorporates OOD detection to distinguish unknown (OOD) from known (in-distribution, ID) classes and predict labels for ID data, thereby enhancing existing subtomogram classification methods. Within this framework, we develop a noise-robust classification method that integrates a 3D discrete wavelet transform-based encoder to reduce high-frequency noise and extract robust features. Additionally, we incorporate a Mahalanobis distance-based OOD detector with a reliable metric for 3D subtomograms and introduce an adaptive classifier that adjusts to accommodate datasets of varying scales. The experimental and visualization results demonstrate that our noise-robust method improves subtomogram classification accuracy and effectively models features while enhancing OOD detection. AVAILABILITY AND IMPLEMENTATION: Our code is available at https://github.com/yxs1137/Subtomo-Classification-with-OOD.git. The real data used in this study can be accessed through CryoET Data Portal.