Video denoising is a fundamental task in low-level computer vision. Most existing denoising algorithms use synthetic data learning. However, there is a significant difference between the noise distributions of synthetic and natural data, which leads to poor generalization performance of the model in actual scenes. Hence, a video method based on an offset optical flow-guided transformer is proposed. The proposed method adopts a semisupervised framework to improve the model’s generalization performance, designs the offset optical flow to guide the transformer in capturing critical information, and performs global self-similarity modeling using neighboring spatiotemporal domain features to improve the denoising performance. In addition, contrastive learning is introduced in the supervised branch to prevent the fitting of wrong labels, imaging prior information to mine sequence features in the unsupervised branch, and a two-branch memory loss is introduced to reduce the difference of double branch training. Experimental results on synthetic and real videos demonstrate that our method has obvious quantitative and qualitative improvements over state-of-the-art methods with fewer parameters.
Aiming at the problems of poor detail texture features, low contrast and poor target detection effect in infrared images, a multi-scale infrared salient target detection algorithm based on anchor free network ECA-YOLOv5 is proposed. The model first introduces ECA attention mechanism to reduce noise interference and improve the infrared feature extraction capability of the network. Then, the shallow high-resolution feature information is supplemented, and the multi-scale feature fusion module is rebuilt to improve the utilization of feature information. Finally, an enhanced feature map output scale is added to further expand the infrared target detection size range and improve the detection capability. The experimental results show that, compared with the relevant detection model, the detection accuracy of this model for infrared targets has been significantly improved, and the recall rate and accuracy have been improved.
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