Abstract:

Recent researches have shown that deep convolutional neural networks can significantly boost the performance of Single-image super-resolution (SISR). In particular, residual network and densely convolutional network can improve performance remarkably. Both path topologies are proposed to alleviate the vanishing-gradient problem of deep convolution networks. Since the residual network enables feature re-usage and the dense skip connections enables new features exploration. A dual path network is proposed for single-image super-resolution by combining the residual network and the dense skip connections in a very deep network. In the proposed network, the feature maps are split into two paths, one path is propagated in the form of residual, and another path is propagated by dense skip connections. In addition, the deconvolution layers are integrated into the network to upscale the feature map which can significantly speedup the network, and the mapping is learn from the low-resolution image to the high-resolution image directly. The network is evaluated with four benchmark datasets. The simulation results demonstrate that the proposed network has much higher peak signal-to-noise ratio(PSNR), in contrast to most conventional state-of-art methods.

Key words: information processing technology, super resolution, residual network, densely convolutional network, convolutional neural network, peak signal-to-noise ratio(PSNR)