Most　existing　image　super-resolution　(SR)　methods　commonly　assume　that　the　degradation　kernel　is　fixed　and　known.　Blind　SR　aims　to　handle　various　unknown　degradation　processes　closer　to　real-world　applications　and　more　generalizations.　We　propose　a　self-supervised　cross-scale　nonlocal　attention　network　for　blind　SR　(CNSR)　which　jointly　models　a　blur　kernel　estimation　module　(KEM)　based　on　a　regularization　model　and　a　high-resolution　image　reconstruction　module　(HRM)　based　on　a　deep　neural　network.　The　low-resolution　(LR)　image　is　used　as　the　supervision　signal,　and　the　blur　kernel　and　high-resolution　image　are　estimated　simultaneously　by　iterating　the　two　modules　alternately.　In　HRM,　we　introduce　a　cross-scale　nonlocal　correspondence　aggregation　module　(CNCAM)　that　uses　the　cross-scale　self-similarity　of　images　to　provide　additional　information　for　image　reconstruction.　Experimental　results　show　that　CNSR　can　effectively　improve　image　reconstruction　performance.