论文标题
根据空间频率变换和深度学习技术研究图像应用
Investigating Image Applications Based on Spatial-Frequency Transform and Deep Learning Techniques
论文作者
论文摘要
这是巴黎电信项目中PRIM项目的报告。该报告是关于基于空间频率变换和深度学习技术的应用程序。在本报告中，有两项主要作品。第一项工作是关于基于深度学习的增强的JPEG压缩方法。我们提出了一种新颖的方法，可以通过在发件人末端传输较少的图像数据来高度增强JPEG压缩。在接收器的末尾，我们提出了DC恢复算法以及深度残留学习框架，以高质量恢复图像。第二项工作是关于基于信号处理的对抗示例的防御。我们提出了小波扩展方法来扩展图像数据特征，这使得生成对抗性示例变得更加困难。我们进一步采用小波deNosing，以减少对抗性扰动的影响。通过密集的实验，我们证明了这两种作品在其应用方案中都是有效的。
This is the report for the PRIM project in Telecom Paris. This report is about applications based on spatial-frequency transform and deep learning techniques. In this report, there are two main works. The first work is about the enhanced JPEG compression method based on deep learning. we propose a novel method to highly enhance the JPEG compression by transmitting fewer image data at the sender's end. At the receiver's end, we propose a DC recovery algorithm together with the deep residual learning framework to recover images with high quality. The second work is about adversarial examples defenses based on signal processing. We propose the wavelet extension method to extend image data features, which makes it more difficult to generate adversarial examples. We further adopt wavelet denoising to reduce the influence of the adversarial perturbations. With intensive experiments, we demonstrate that both works are effective in their application scenarios.
