鉴别器
计算机科学
发电机(电路理论)
图像(数学)
翻译(生物学)
人工智能
图像翻译
感知
计算机视觉
模式识别(心理学)
物理
信使核糖核酸
基因
探测器
生物
功率(物理)
神经科学
化学
电信
生物化学
量子力学
作者
Yanyun Qu,Yizi Chen,Jingying Huang,Yuan Xie
标识
DOI:10.1109/cvpr.2019.00835
摘要
In this paper, we reduce the image dehazing problem to an image-to-image translation problem, and propose Enhanced Pix2pix Dehazing Network (EPDN), which generates a haze-free image without relying on the physical scattering model. EPDN is embedded by a generative adversarial network, which is followed by a well-designed enhancer. Inspired by visual perception global-first theory, the discriminator guides the generator to create a pseudo realistic image on a coarse scale, while the enhancer following the generator is required to produce a realistic dehazing image on the fine scale. The enhancer contains two enhancing blocks based on the receptive field model, which reinforces the dehazing effect in both color and details. The embedded GAN is jointly trained with the enhancer. Extensive experiment results on synthetic datasets and real-world datasets show that the proposed EPDN is superior to the state-of-the-art methods in terms of PSNR, SSIM, PI, and subjective visual effect.
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