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Restoration of poor quality images with a blended set of artifacts plays avital role for a reliable diagnosis. Existing studies have focused on specificrestoration problems such as image deblurring, denoising, and exposurecorrection where there is usually a strong assumption on the artifact type andseverity. As a pioneer study in blind X-ray restoration, we propose a jointmodel for generic image restoration and classification: Restore-to-ClassifyGenerative Adversarial Networks (R2C-GANs). Such a jointly optimized modelkeeps any disease intact after the restoration. Therefore, this will naturallylead to a higher diagnosis performance thanks to the improved X-ray imagequality. To accomplish this crucial objective, we define the restoration taskas an Image-to-Image translation problem from poor quality having noisy,blurry, or over/under-exposed images to high quality image domain. The proposedR2C-GAN model is able to learn forward and inverse transforms between the twodomains using unpaired training samples. Simultaneously, the jointclassification preserves the disease label during restoration. Moreover, theR2C-GANs are equipped with operational layers/neurons reducing the networkdepth and further boosting both restoration and classification performances.The proposed joint model is extensively evaluated over the QaTa-COV19 datasetfor Coronavirus Disease 2019 (COVID-19) classification. The proposedrestoration approach achieves over 90% F1-Score which is significantly higherthan the performance of any deep model. Moreover, in the qualitative analysis,the restoration performance of R2C-GANs is approved by a group of medicaldoctors. We share the software implementation athttps://github.com/meteahishali/R2C-GAN.

R2C-GAN: Restore-to-Classify GANs for Blind X-Ray Restoration and COVID-19 Classification