Stochastic restoration algorithms allow to explore the space of solutions that correspond to the degraded input. In this paper we reveal additional fundamental advantages of stochastic methods over deterministic ones, which further motivate their use. First, we prove that any restoration algorithm that attains perfect perceptual quality and whose outputs are consistent with the input must be a posterior sampler, and is thus required to be stochastic. Second, we illustrate that while deterministic restoration algorithms may attain high perceptual quality, this can be achieved only by filling up the space of all possible source images using an extremely sensitive mapping, which makes them highly vulnerable to adversarial attacks. Indeed, we show that enforcing deterministic models to be robust to such attacks profoundly hinders their perceptual quality, while robustifying stochastic models hardly influences their perceptual quality, and improves their output variability. These findings provide a motivation to foster progress in stochastic restoration methods, paving the way to better recovery algorithms.
翻译:斯托卡恢复算法允许探索与退化输入相对应的解决方案空间。 在本文中,我们揭示了比确定性输入法更能促进其使用的其他基本优势。 首先,我们证明任何能够达到完美感知质量且其产出与输入一致的恢复算法都必须是后方采样器,因此必须具有随机性。 其次,我们说明虽然确定性恢复算法可能达到高感知质量,但只有通过使用极其敏感的绘图来填补所有可能的源图像的空间才能实现这一点,这使得它们极易受到对抗性攻击的伤害。 事实上,我们表明,实施确定性模型来对付这种攻击会大大妨碍其感知质量,同时稳健的随机性模型不会影响其感知质量,而且会提高产出的可变性。 这些发现提供了一种动力,可以促进在随机性恢复方法方面取得进展,为更好的恢复算法铺平了道路。