Restoration of poor quality images with a blended set of artifacts plays a vital role for a reliable diagnosis. Existing studies have focused on specific restoration problems such as image deblurring, denoising, and exposure correction where there is usually a strong assumption on the artifact type and severity. As a pioneer study in blind X-ray restoration, we propose a joint model for generic image restoration and classification: Restore-to-Classify Generative Adversarial Networks (R2C-GANs). Such a jointly optimized model keeps any disease intact after the restoration. Therefore, this will naturally lead to a higher diagnosis performance thanks to the improved X-ray image quality. To accomplish this crucial objective, we define the restoration task as an Image-to-Image translation problem from poor quality having noisy, blurry, or over/under-exposed images to high quality image domain. The proposed R2C-GAN model is able to learn forward and inverse transforms between the two domains using unpaired training samples. Simultaneously, the joint classification preserves the disease label during restoration. Moreover, the R2C-GANs are equipped with operational layers/neurons reducing the network depth and further boosting both restoration and classification performances. The proposed joint model is extensively evaluated over the QaTa-COV19 dataset for Coronavirus Disease 2019 (COVID-19) classification. The proposed restoration approach achieves over 90% F1-Score which is significantly higher than the performance of any deep model. Moreover, in the qualitative analysis, the restoration performance of R2C-GANs is approved by a group of medical doctors. We share the software implementation at https://github.com/meteahishali/R2C-GAN.

翻译：修复质量差的图像, 并配有混合的人工制品集( R2C- GANs ) 。 这样的联合优化模型可以在恢复后保持任何疾病的质量完整。 因此, 由于X光图像质量的提高, 现有的研究自然会提高诊断性能。 为了实现这一关键目标, 我们将修复任务定义为从质量差的图像到图像的转换问题, 从噪音、 模糊或过低的图像到高品质图像域。 提议的 R2C- GAN 模型能够学习两个领域之间的前向和反向变化, 并且使用不完善的培训样本。 同时, 联合分类可以保存恢复期间的疾病标签。 此外, R2C- GANS 的升级模型 QQ- GANS 将恢复性能定义为从质量差的图像到图像的转换问题, 噪音、 模糊或超低/ 外延图像到高品质域域域。 拟议的 R2C- GAN 模型能够通过未更新的培训方法在两个领域之间学习进反向和反向变换。 在恢复过程中, R2C- GANSO1 数据库中, 正在进一步降低运行的恢复性能 。