Dense U-net for super-resolution with shuffle pooling layer
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Single image super-resolution (SISR) in unconstrained environments is challenging because of various illuminations, occlusion and complex environments. Recent researches have achieved great progress on super-resolution due to the development of deep learning in the field of computer vision. In this letter, a Dense U-net with shuffle pooling method is proposed. First, a modified U-net with dense blocks, called dense U-net, is proposed for SISR. Second, a novel pooling strategy called shuffle pooling is designed, which is applied to the dense U-Net for super-resolution task. Third, a mix loss function, which combined with Mean Square Error(MSE), Structural Similarity Index (SSIM) and Mean Gradient Error (MGE), is proposed to solve the perception loss and high-frequency information loss. The proposed method achieves superior accuracy over previous state-of-the-arts on the three benchmark datasets: SET14, BSD300, ICDAR2003. Code is available online.
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