Multi–grid back–projection (MGBP) is a fully–convolutional network architecture that can learn to restore images and videos with upscaling artifacts. Using the same strategy of multi–grid partial differential equation (PDE) solvers this multiscale architecture scales computational complexity efficiently with increasing output resolutions. The basic processing block is inspired in the iterative back–projection (IBP) algorithm and constitutes a type of cross–scale residual block with feedback from low resolution references. The architecture performs in par with state–of–the-arts alternatives for regression targets that aim to recover an exact copy of a high resolution image or video from which only a downscale image is known. A perceptual quality target aims to create more realistic outputs by introducing artificial changes that can be different from a high resolution original content as long as they are consistent with the low resolution input. For this target we propose a strategy using noise inputs in different resolution scales to control the amount of artificial details generated in the output. The noise input controls the amount of innovation that the network uses to create artificial realistic details. The effectiveness of this strategy is shown in benchmarks and it is explained as a particular strategy to traverse the perception–distortion plane.

中文翻译：

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多网格反投影网络

多网格反投影（MGBP）是一种完全卷积的网络体系结构，可以学习还原具有放大伪影的图像和视频。使用相同的多网格偏微分方程（PDE）求解器策略，该多尺度体系结构可随着输出分辨率的提高有效地扩展计算复杂性。基本处理块是从迭代反投影（IBP）算法中获得灵感的，它构成了一种跨尺度残差块，具有来自低分辨率参考的反馈。该体系结构的性能与最新的回归目标替代方案相同，这些替代目标旨在恢复高分辨率图像或视频的精确副本，而从中仅知道缩小图像。感知质量目标的目的是通过引入可能与高分辨率原始内容不同的人为更改来创建更现实的输出，只要它们与低分辨率输入一致即可。对于此目标，我们提出了一种使用不同分辨率等级的噪声输入来控制输出中生成的人工细节的数量的策略。噪声输入控制网络用于创建人造现实细节的创新量。基准中显示了该策略的有效性，并被解释为一种遍历感知扭曲平面的特殊策略。对于此目标，我们提出了一种使用不同分辨率等级的噪声输入来控制输出中生成的人工细节的数量的策略。噪声输入控制网络用于创建人造现实细节的创新量。基准中显示了该策略的有效性，并被解释为一种遍历感知扭曲平面的特殊策略。对于此目标，我们提出了一种使用不同分辨率等级的噪声输入来控制输出中生成的人工细节的数量的策略。噪声输入控制网络用于创建人造现实细节的创新量。基准中显示了该策略的有效性，并被解释为一种遍历感知扭曲平面的特殊策略。