Image Completion

Complete an image whose 99% pixels are randomly missing

This work addresses extreme image completion where 99% of pixels are randomly absent. The method formulates the problem as low-rank matrix recovery, exploiting non-local self-similarity and sparsity priors to reconstruct plausible image content from the tiny fraction of observed pixels, outperforming prior inpainting approaches under this highly degraded setting.