Super-resolution based generative adversarial network using visual
perceptual loss function
release_gy2uztvkozgmlhfpzaejb4zbwy
by
Xuan Zhu and Yue Cheng and Rongzhi Wang
2019
Abstract
In recent years, perceptual-quality driven super-resolution methods show
satisfactory results. However, super-resolved images have uncertain texture
details and unpleasant artifact. We build a novel perceptual loss function
composed of morphological components adversarial loss and color adversarial
loss and salient content loss to ameliorate these problems. The adversarial
loss is applied to constrain color and morphological components distribution of
super-resolved images and the salient content loss highlights the perceptual
similarity of feature-rich regions. Experiments show that proposed method
achieves significant improvements in terms of perceptual index and visual
quality compared with the state-of-the-art methods.
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