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Recently, single-image super-resolution has made great progress due to the development of deep convolutional neural networks (CNNs). The vast majority of CNN-based models use a predefined upsampling operator, such as bicubic interpolation, to upscale input low-resolution images to the desired size and learn nonlinear mapping between the interpolated image and ground truth high-resolution (HR) image. However, interpolation processing can lead to visual artifacts as details are over smoothed, particularly when the super-resolution factor is high. In this paper, we propose a deep recurrent fusion network (DRFN), which utilizes transposed convolution instead of bicubic interpolation for upsampling and integrates different-level features extracted from recurrent residual blocks to reconstruct the final HR images. We adopt a deep recurrence learning strategy and, thus, have a larger receptive field, which is …