Recently, deep convolutional neural networks (CNNs) have been attracting considerable attention in single image super-resolution. Some CNN-based methods, such as VDSR verified that residual learning can speed up the training and significantly improve the performance of accuracy. However, with very deep networks, convergence speed is still a critical issue in training due to the cost of requiring enormous parameters. In order to deal with this issue, we redesign the residual networks based on dilated networks. In this paper, we propose symmetrical dilated residual convolution networks (FDSR) to tackle image super-resolution problems. Our network is on the basis of the dilated convolutions supported exponential expansion of the receptive field without loss of resolution and coverage. This means that FDSR can speed up the training and improve the performance of accuracy without increasing the model's depth or complexity. Meanwhile, we attempt to combine the image pre-processing approach of VGG-net with mean squared error (MSE) to enhance the performance. The experimental results demonstrate that the training time-consuming proposed model achieves nearly a half with even superior restoration quality. Further, we present a novel network with less layers and parameters can achieve real-time performance on a generic CPU and still maintain superior performance. INDEX TERMS Image super-resolution, residual learning, dilated convolution.

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Infrared image target recognition provides an important means of night traffic management and battlefield environment monitoring. With the improvement of the performance of infrared sensors and the popularization of applications, it becomes possible to obtain multiview infrared images of the same target in the same scene. A target recognition method combining multiview infrared images is proposed. At first, the internal correlation analysis of multiview infrared images is performed based on the nonlinear correlation information entropy (NCIE). The view subset from all the multiview images with the largest NCIE is selected as candidate samples for the subsequent target recognition. The joint sparse representation (JSR) is used to classify all infrared images in the candidate view subset. JSR can effectively investigate the internal correlation of multiple related sparse representation problems and improve the reconstruction accuracy and classification capabilities. In the experiments, the tests are performed on the collected infrared images of multiple types of traffic vehicles, under the conditions of original, noisy, and occluded samples. The effectiveness and robustness of the proposed method can be verified by comparative analysis.

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