论文标题
通过通过LR和HR过程建模GAN的真实图像超分辨率
Real Image Super-Resolution using GAN through modeling of LR and HR process
论文作者
论文摘要
当前现有的深层图像超分辨率方法通常假定低分辨率（LR）图像是对高分辨率（HR）图像的双尺度降低的。但是，这种理想的双色下采样过程与真实的LR降解不同，这些降解通常来自不同降解过程的复杂组合，例如摄像机模糊，传感器噪声，锐化工件，JPEG压缩和进一步的图像编辑，以及在互联网上的图像传输和几次图像传输和单个可估计的noises。它导致了逆上尺度问题的高度不良的性质。为了解决这些问题，我们提出了一种基于GAN的SR方法，该方法通过直接学习降解分布，然后合成配对的LR/HR训练数据，以将LR和SR模型中纳入LR和SR模型中的可学习的自适应正弦非线性，以训练广义的SR模型以实际图像降级。我们证明了我们提出的方法在定量和定性实验中的有效性。
The current existing deep image super-resolution methods usually assume that a Low Resolution (LR) image is bicubicly downscaled of a High Resolution (HR) image. However, such an ideal bicubic downsampling process is different from the real LR degradations, which usually come from complicated combinations of different degradation processes, such as camera blur, sensor noise, sharpening artifacts, JPEG compression, and further image editing, and several times image transmission over the internet and unpredictable noises. It leads to the highly ill-posed nature of the inverse upscaling problem. To address these issues, we propose a GAN-based SR approach with learnable adaptive sinusoidal nonlinearities incorporated in LR and SR models by directly learn degradation distributions and then synthesize paired LR/HR training data to train the generalized SR model to real image degradations. We demonstrate the effectiveness of our proposed approach in quantitative and qualitative experiments.
