It is well-known that a number of excellent super-resolution (SR) methods using convolutional neural networks (CNNs) generate checkerboard artifacts. A condition to avoid the checkerboard artifacts is proposed in this paper. So far, checkerboard artifacts have been mainly studied for linear multirate systems, but the condition to avoid checkerboard artifacts can not be applied to CNNs due to the non-linearity of CNNs. We extend the avoiding condition for CNNs, and apply the proposed structure to some typical SR methods to confirm the effectiveness of the new scheme. Experiment results demonstrate that the proposed structure can perfectly avoid to generate checkerboard artifacts under two loss conditions: mean square error and perceptual loss, while keeping excellent properties that the SR methods have.

It is well-known that a number of convolutional neural networks (CNNs) generate checkerboard artifacts in both of two processes: forward-propagation of upsampling layers and backpropagation of convolutional layers. A condition for avoiding the artifacts is proposed in this paper. So far, these artifacts have been studied mainly for linear multirate systems, but the conventional condition for avoiding them cannot be applied to CNNs due to the non-linearity of CNNs. We extend the avoidance condition for CNNs and apply the proposed structure to typical CNNs to confirm whether the novel structure is effective. Experimental results demonstrate that the proposed structure can perfectly avoid generating checkerboard artifacts while keeping the excellent properties that CNNs have.

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