Astronomical image deconvolution has evolved from early linear regularization to modern deep learning approaches. This article provides a concise overview of the historical milestones, methodological breakthroughs, and current challenges in the field, with emphasis on sparsity, compressed sensing, and the role of deep learning in large astronomical surveys.