Blind　image　deconvolution　is　the　problem　of　recovering　the　latent　image　from　the　only　observed　blurry　image　when　the　blur　kernel　is　unknown.　In　this　paper,　we　propose　an　edge-based　blur　kernel　estimation　method　for　blind　motion　deconvolution.　In　our　previous　work,　we　incorporate　both　sparse　representation　and　self-similarity　of　image　patches　as　priors　into　our　blind　deconvolution　model　to　regularize　the　recovery　of　the　latent　image.　Since　almost　any　natural　image　has　properties　of　sparsity　and　multi-scale　self-similarity,　we　construct　a　sparsity　regularizer　and　a　cross-scale　non-local　regularizer　based　on　our　patch　priors.　It　has　been　observed　that　our　regularizers　often　favor　sharp　images　over　blurry　ones　only　for　image　patches　of　the　salient　edges　and　thus　we　define　an　edge　mask　to　locate　salient　edges　that　we　want　to　apply　our　regularizers.　Experimental　results　on　both　simulated　and　real　blurry　images　demonstrate　that　our　method　outperforms　existing　state-of-the-art　blind　deblurring　methods　even　for　handling　of　very　large　blurs,　thanks　to　the　use　of　the　edge　mask.　©　2021,　Springer　Nature　Switzerland　AG.