Titlebook: Computer Vision – ECCV 2018 Workshops; Munich, Germany, Sep Laura Leal-Taixé,Stefan Roth Conference proceedings 2019 Springer Nature Switze

慢跑

鉆孔

無關(guān)緊要

極小量

Bi-GANs-ST for Perceptual Image Super-Resolutionetrics, ., PSNR and SSIM, but these indices cannot provide suitable results in accordance with the perception of human being. Recently, a more reasonable perception measurement has been proposed in [.], which is also adopted by the PIRM-SR 2018 challenge. In this paper, motivated by [.], we aim to g

MEAN

Multi-modal Spectral Image Super-Resolution patches. However, these methods only take a single-scale image as input and require large amount of data to train without the risk of overfitting. In this paper, we tackle the problem of multi-modal spectral image super-resolution while constraining ourselves to a small dataset. We propose the use

CBC471

Generative Adversarial Network-Based Image Super-Resolution Using Perceptual Content Lossesd on good performance of a recently developed model for super-resolution, i.e., deep residual network using enhanced upscale modules (EUSR) [.], the proposed model is trained to improve perceptual performance with only slight increase of distortion. For this purpose, together with the conventional c

Dislocation

gustation