Abstract:
Convolutional neural networks have recently been shown to have the highest accuracy for single image super-resolution (SISR) reconstruction. Most of the network structures suffer from low training and reconstruction speed, and still have the problem that one model can only be rebuilt for a single scale. For these problems, a deep cascaded network (DCN) is designed to reconstruct the image step by step. L2 and the perception loss function are used to optimize the network together, and then a high quality reconstructed image will be obtained under the joint action of each cascade. In addition, our network can get reconstructions of different scales, such as 1.5×, 2×, 2.5×, 3×, 3.5× and 4×. Extensive experiments on several of the largest benchmark datasets demonstrate that the proposed approach performs better than existing methods in terms of accuracy and visual improvement.
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Abstract: Convolutional neural networks have recently been shown to have the highest accuracy for single image super-resolution (SISR) reconstruction. Most of the network structures suffer from low training and reconstruction speed, and still have the problem that one model can only be rebuilt for a single scale. For these problems, a deep cascaded network (DCN) is designed to reconstruct the image step by step. L2 and the perception loss function are used to optimize the network together, and then a high quality reconstructed image will be obtained under the joint action of each cascade. In addition, our network can get reconstructions of different scales, such as 1.5×, 2×, 2.5×, 3×, 3.5× and 4×. Extensive experiments on several of the largest benchmark datasets demonstrate that the proposed approach performs better than existing methods in terms of accuracy and visual improvement.
