GRAM: Gradient Rescaling Attention Model for Data Uncertainty Estimation in Single Image Super Resolution

In this paper, a new learning method to quantify data uncertainty without suffering from performance degradation in Single Image Super Resolution (SISR) is proposed. Our work is motivated by the fact that the idea of loss design for capturing uncertainty and that for solving SISR are contradictory. As to capturing data uncertainty, we often model the output of a network as a Euclidian distance divided by a predictive variance, negative log-likelihood (NLL) for the Gaussian distribution, so that images with high variance have less impact on training. On the other hand, in the SISR domain, recent works give more weights to the loss of challenging images to improve the performance by using attention models. Nonetheless, the conflict should be handled to make neural networks capable of predicting the uncertainty of a super-resolved image, without suffering from performance degradation. Therefore, we propose a method called Gradient Rescaling Attention Model (GRAM) that combines both attempts effectively. Since variance may reflect the difficulty of an image, we rescale the gradient of NLL by the degree of variance. Hence, the neural network can focus on the challenging images, similarly to attention models. We conduct performance evaluation using standard SISR benchmarks in terms of peak signal-noise ratio (PSNR) and structural similarity (SSIM). The experimental results show that the proposed gradient rescaling method generates negligible performance degradation compared to SISR outputs with the Euclidian loss, whereas NLL without attention degrades the SR quality.