Face image super-resolution imaging is an important technology which can be utilized in crime scene investigations and public security. Modern CNN-based super-resolution produces excellent results in terms of peak signal-to-noise ratio and the structural similarity index (SSIM). However, perceptual quality is generally poor, and the details of the facial features are lost. To overcome this problem, we propose a novel deep neural network to predict the super-resolution wavelet coefficients in order to obtain clearer facial images. Firstly, this paper uses prior knowledge of face images to manually emphases relevant facial features with more attention. Then, a linear low-rank convolution in the network is used. Finally, image edge features from canny detector are applied to enhance super-resolution images during training. The experimental results show that the proposed method can achieve competitive PSNR and SSIM and produces images with much higher perceptual quality.

中文翻译：

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人脸超分辨率的学习小波系数

人脸图像超分辨率成像是一项重要的技术，可用于犯罪现场调查和公共安全。基于现代 CNN 的超分辨率在峰值信噪比和结构相似性指数 (SSIM) 方面产生了出色的结果。但是，感知质量普遍较差，丢失了五官的细节。为了克服这个问题，我们提出了一种新颖的深度神经网络来预测超分辨率小波系数，以获得更清晰的面部图像。首先，本文利用人脸图像的先验知识来手动强调相关的面部特征，更加注意。然后，使用网络中的线性低秩卷积。最后，在训练过程中应用来自 Canny 检测器的图像边缘特征来增强超分辨率图像。