Image　restoration　is　a　core　problem　in　computer　vision　and　image　processing.　In　this　paper,　we　introduce　a　unified　low-patch-rank　minimization　model,　which　possesses　one　nuclear　norm　regularization　term　promoting　the　low-patch-rankness,　and　two　sparse　regularization　terms　including　the　classical　total　variation　(TV)　norm　and　a　general　sparse　term　under　certain　transform　such　as　discrete　cosine　transform.　By　setting　balancing　parameters,　our　unified　model　reduces　to　the　classical　TV-regularized　low-patch-rank　minimization　model　and　yields　a　new　non-TV-regularized　low-patch-rank　prior　image　restoration　model.　Due　to　the　multi-block　structure　of　the　model,　we　introduce　a　three-block　alternating　minimization　algorithm　to　find　approximate　solutions　of　the　proposed　models.　A　series　of　computational　results　on　image　inpainting　and　deblurring　further　show　that　our　approaches　are　reliable　to　recover　high-quality　images　from　degraded　ones.　©　2022　Elsevier　Inc.