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With the development of technology, we have increasing demand for high-quality images in many area. But existing imaging systems are limited by hardware limitations such as the density of their inherent sensor arrays, the image resolution sometimes fails to meet the requirements. Super-resolution technology provides a solution to improve the quality of image from the software aspect without changing the hardware device. In terms of the number of images processed in superresolution area, we generally divided super-resolution into single-image super-resolution reconstruction and multi-image super-resolution reconstruction. Limited by the details contained in the input image itself, single-image super-resolution reconstruction is often difficult to achieve the desired effect.Compared with the single-image super-resolution algorithm, the multi-image super-resolution algorithm has the advantage of making full use of the complementary information between the image sequences. Therefore, multi-image super-resolution algorithm tends to have a stronger super-resolution capability. However, it is very difficult to capture the image sequence of the same scene with complementary information. Camera array system can capture the same scene from multiple perspectives, which can better solve the problem of image sequence acquisition with complementary information. Because most of the current methods about camera array are based on traditional methods , we propose a multi-view images from camera array super-resolution with deep learning method. In this paper , we use a set of multi-view images as input. Then, feature of each image is refined with redundant information(i.e., angular information) from that of its adjacent images by Local Feature Comparison (LFC) module. Feature improved will be aggregated by Global Feature Fusion (GFF) module we proposed. Finally, we can get only one high-resolution image via high-resolution reconstruction module. Benefiting from way, the proposed method achieves better visual quality with more realistic and natural textures.
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