In recent years, computer vision is more and more widely used in the field of intelligent control, and super-resolution reconstruction technology solves the problem of making the image clear. In order to solve the problem of insufficient image feature extraction and low information dissemination efficiency in the FSRCNN algorithm, this paper puts forward a dual-branch and residual network for image super-resolution reconstruction. The model designs a dual-branch feature extraction channel, expands the feature extraction channels, and improves the high-frequency information extraction ability of the input image; adopts an improved residual block to reduce the loss of information transmission. As shown in the experimental results, the peak signal-to-noise ratio (PSNR) of the Set5 dataset is 0.14dB and 0.52dB higher than that of the FSRCNN algorithm under the 2 and 3 scale factors, and the Set14 dataset is 0.13dB and 0.41dB higher respectively.
Some tracking algorithms based on Siamese network have made great progress in similarity learning via features cross-correlation between an object branch and a search branch. However, it is significantly challenging for object tracking in video sequences in terms of target deformation with greatly varying. We propose a Siamese network based on global and local feature matching for object tracking including three phases with the aim of addressing the above issues. In the first phase, obtaining the global similarity matching and local relational mapping similarity of the template branch and the search branch by a selection mechanism of object template-aware features are to reduce the impact of background features on the local matching. In the second phase, introducing correlation matching of the local feature for establishing correspondence among partial-level pixels. Finally, combining the classification and regression results with global matching features and local matching features in a weighted fusion. Extensive experiments are conducted on datasets (OTB-100, LaSOT and GOT-10K) demonstrate that the proposed network enables to achieve superiority compared against the state-of-the-art method and provides an efficient scenario for tackling the issue.
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