Compression artifacts arise in images whenever a lossy compression algorithm
is applied. These artifacts eliminate details present in the original image, or
add noise and small structures; because of these effects they make images less
pleasant for the human eye, and may also lead to decreased performance of
computer vision algorithms such as object detectors. To eliminate such
artifacts, when decompressing an image, it is required to recover the original
image from a disturbed version. To this end, we present a feed-forward fully
convolutional residual network model trained using a generative adversarial
framework. To provide a baseline, we show that our model can be also trained
optimizing the Structural Similarity (SSIM), which is a better loss with
respect to the simpler Mean Squared Error (MSE). Our GAN is able to produce
images with more photorealistic details than MSE or SSIM based networks.
Moreover we show that our approach can be used as a pre-processing step for
object detection in case images are degraded by compression to a point that
state-of-the art detectors fail. In this task, our GAN method obtains better
performance than MSE or SSIM trained networks.

利用生成对抗网络模型和卷积残差网络结构，该论文提出了一种用于去除图像压缩失真的方法，并且该方法在物体检测方面的性能表现优于传统的均方误差和结构相似性优化算法。