Inspired by the free-energy brain theory, which implies that human visual system (HVS) tends to reduce uncertainty and restore perceptual details upon seeing a distorted image, we propose restorative adversarial net (RAN), a GAN-based model for no-reference image quality assessment (NR-IQA). RAN, which mimics the process of HVS, consists of three components: a restorator, a discriminator and an evaluator. The restorator restores and reconstructs input distorted image patches, while the discriminator distinguishes the reconstructed patches from the pristine distortion-free patches. After restoration, we observe that the perceptual distance between the restored and the distorted patches is monotonic with respect to the distortion level. We further define Gain of Restoration (GoR) based on this phenomenon. The evaluator predicts perceptual score by extracting feature representations from the distorted and restored patches to measure GoR. Eventually, the quality score of an input image is estimated by weighted sum of the patch scores. Experimental results on Waterloo Exploration, LIVE and TID2013 show the effectiveness and generalization ability of RAN compared to the state-of-the-art NR-IQA models.

通过模拟人类视觉系统的过程，我们提出了一种无参考图像质量评估系统——Restorative Adversarial Net，该系统包括复原器、鉴别器和评估器三个部分，通过提取特征来计算Gain of Restoration并将其结合用于评估输入图片的质量，实验证明该系统相比其他先进的NR-IQA模型具有更高的效率和泛化能力。

RAN4IQA: 无参考图像质量评估的恢复性对抗网络