Image segmentation technology is an important branch of visual understanding system, with contributing to promote the accuracy of classification. In recent years, the integration of deep learning and image processing has produced a new generation of image segmentation algorithms. Compared with traditional methods, the performance has been improved and reached high accuracy. This work mainly introduces traditional or based on deep learning and compares the performance of related algorithms. To solve the problems of existing algorithms, the future development trend is prospected.
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.
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