In recent years, significant progress has been made in single-image super-resolution with the advancements of deep convolutional neural networks (CNNs) and transformer-based architectures. These two techniques have led the way in the field of super-resolution technology research. However, performance improvements often come at the cost of a substantial increase in the number of parameters, thereby limiting the practical applications of super-resolution methods. Existing lightweight super-resolution methods, which primarily focus on single-scale feature extraction, lead to the issue of missing multi-scale features. This results in incomplete feature acquisition and poor reconstruction of the image. In response to these challenges, this paper proposed a lightweight multi-scale feature cooperative enhancement network (MFCEN). The network consists of three parts: shallow feature extraction, deep feature extraction, and image reconstruction. In the deep feature extraction part, a novel integrated multi-level feature module was introduced. Compared to existing CNN and transformer hybrid super-resolution networks, MFCEN significantly reduced the number of parameters while maintaining performance. This improvement was particularly evident at a scale factor of 3. The network introduced a novel comprehensive integrated multi-level feature module, leveraging the strong local perceptual capabilities of CNNs and the superior global information processing of transformers. It was designed with depthwise separable convolutions for extracting local information and a block-scale and global feature extraction module based on vision transformers (ViTs). While extracting the three scales of features, a satisfiability attention mechanism with a feed-forward network that can control the information was used to keep the network lightweight. Experiments demonstrated that the proposed model surpasses the reconstruction performance of the 498K-parameter SPAN model with a mere 488K parameters. Extensive experiments on commonly used image super-resolution datasets further validated the effectiveness of the network.

Bird activities like nesting and perching in transformer substations threaten power grid stability by causing short circuits and insulation failures. Existing bird repelling devices are inefficient due to the lack of accurate detection and positioning, leading to energy waste and safety hazards from continuous operation. To address this, this paper develops a tiny bird detection and location system guided by heterogeneous binocular images for precise, targeted repulsion. For well-lit scenarios, a two-stage contextual information enhancement network is proposed. It mines multiscale context to highlight tiny bird regions, fuses context with second-stage features via channel dimension enhancement, and uses spatial attention for accurate localization. For low-light or occluded scenes, a multiscale contextual feature enhancement network processes infrared images, adopting multibranch cross-level feature fusion and combining transformer with multisize convolution to suppress background and thermal radiation interference. Additionally, heterogeneous binocular cameras are calibrated to calculate bird spatial distance, integrating detection results with spatial information to drive a laser repelling device. Experimental results in real substation environments show the system meets engineering requirements for robustness and accuracy. The detection in visible images achieves an overall average precision of $59.8$%, while the infrared detection outperforms advanced algorithms in key metrics. The spatial localization error is controlled within $4.9$%, significantly improving bird expulsion success rate and reducing energy consumption. This work provides a reliable technical solution for safeguarding power grid operation and offers valuable references for tiny object detection in complex industrial scenarios.