In recent years, advancements in deep Convolutional Neural Networks (CNNs) have brought about a paradigm shift in the realm of image super-resolution (SR). While augmenting the depth and breadth of CNNs can indeed enhance network performance, it often comes at the expense of heightened computational demands and greater memory usage, which can restrict practical deployment. To mitigate this challenge, we have incorporated a technique called factorized convolution and introduced the efficient Cross-Scale Interaction Block (CSIB). CSIB employs a dual-branch structure, with one branch extracting local features and the other capturing global features. Interaction operations take place in the middle of this dual-branch structure, facilitating the integration of cross-scale contextual information. To further refine the aggregated contextual information, we designed an Efficient Large Kernel Attention (ELKA) using large convolutional kernels and a gating mechanism. By stacking CSIBs, we have created a lightweight cross-scale interaction network for image super-resolution named “CSINet”. This innovative approach significantly reduces computational costs while maintaining performance, providing an efficient solution for practical applications. The experimental results convincingly demonstrate that our CSINet surpasses the majority of the state-of-the-art lightweight super-resolution techniques used on widely recognized benchmark datasets. Moreover, our smaller model, CSINet-S, shows an excellent performance record on lightweight super-resolution benchmarks with extremely low parameters and Multi-Adds (e.g., 33.82 dB@Set14 × 2 with only 248 K parameters).

Industrial image anomaly detection (IAD) is crucial for quality control, but its performance often degrades when training data contain label noise. To circumvent the reliance on potentially flawed labels, unsupervised methods that learn from the data distribution itself have become a mainstream approach. Among various unsupervised techniques, student–teacher frameworks have emerged as a highly effective paradigm. Student–Teacher Feature Pyramid Matching (STPM) is a powerful method within this paradigm, yet it is susceptible to such noise. Inspired by STPM and aiming to solve this issue, this paper introduces Meta-Learning Guided STPM (MLG-STPM), a novel framework that enhances STPM’s robustness by incorporating a guidance mechanism inspired by meta-learning. This guidance is achieved through an Evolving Meta-Set (EMS), which dynamically maintains a small high-confidence subset of training samples identified by their low disagreement between student and teacher networks. By training the student network on a combination of the current batch and the EMS, MLG-STPM effectively mitigates the impact of noisy labels without requiring an external clean dataset or complex re-weighting schemes. Comprehensive experiments on the MVTec AD and VisA benchmark datasets with synthetic label noise (0% to 20%) demonstrate that MLG-STPM significantly improves anomaly detection and localization performance compared to the original STPM, especially under higher noise conditions, and achieves competitive results against other relevant approaches.