This report investigates the digital image processing of image inpainting methods, particularly for digital image reconstruction and restoration through the two computational tools grouped into:  MatLAB/Image Segmenter and  Anaconda/OpenCV/Python. The use cases explored in the project involve image reconstruction and restoration of celestial imageries for means of clear demonstration and subject-matter consistency but can extend to more artistic purposes involving the removal of unwanted objects within the backgrounds or foregrounds of images that can be “erased” or “hidden” by being replaced by neighboring pixels of similar characteristics for image reconstruction and the removal of damaged parts observed in old photographs damaged by noises, dark streaks, faded or scratched edges, folds, physio-chemical alterations, ink blotches, or technological obscurities (such as lens flare, lens aberrations, or crop marks) for image restoration. The celestial images used for the purposes of this project are taken from the public collection of NASA’s James Webb and Hubble Telescope image archives.
Haruka Kido. "Image Inpainting Methods: Digital Image Reconstruction and Restoration" (2023). Electrical Engineering Student Publications. 13.