Machine learning and ai software for image improving and restoring

Disturbance removal in astronomical images is of paramount importance both for automated processing methods and for “manual” inspection of images by humans. Disturbances in images comes in various forms and can be introduced in the raw signal itself as either noise, when random processes are involved, or artifacts, due to signal acquisition modality. Such is the case of signals acquired by earth rotation aperture synthesis where astronomical observations are made by integrating multiple signals coming from arrays of geographically distributed radio antennas. In this work we provide a dataset and present a deep learning nertwork for removing interferometry artifacts in combined images generated by aperture synthesis. The model combines the capabilities of both U-Nets [1] and Vision Transformers [2]. The CANDELS dataset was used as the seed dataset. The 16200×16200 images were randomly sampled by extracting random crops of size 1024×1024. The model was trained on a split comprising 60% of the dataset (60,000 images), 20% (20,000 images) was used for validation and 20% (20,000 images) in testing achieving a PSNR of 18db and a SSIM score of 0.51.