Image　deblurring　refers　to　the　deconvolution　of　blurry　images　and　blur　kernels　to　get　sharp　images.The　process　of　deblurring　when　the　blur　kernel　is　unknown　is　called　blind　deblurring.　In　general,　blind　image　deblurring　includes　two　steps.　One　is　the　estimation　of　blur　kernel,　which　is　actually　estimating　how　images　become　blurry.　The　other　is　to　use　the　blur　kernel　for　image　deconvolution.　In　this　paper,　a　blind　deblurring　method　for　natural　images　is　proposed.　Firstly,　we　take　a　preprocession　and　denoise　the　image　to　eliminate　the　influence　of　noise　on　kernel　estimation　and　avoid　the　noise　being　amplified　in　the　final　image　reconstruction　stage.　Secondly,　we　extract　the　main　structure　of　the　image　by　a　relative　total　variation　method,　next　enhance　the　structure　image　by　the　shock　filter,　and　then　extract　its　high　frequency　layer.　This　method　can　provide　effective　and　significant　edges　for　kernel　estimation.　In　the　kernel　estimation　stage,　we　use　both　the　image　intensity　and　gradient　value　as　regularization　terms　to　ensure　the　sparsity　and　continuity　of　the　blur　kernel.　Finally,　in　image　reconstruction　stage,　we　use　a　simple　hyper-Laplacian　prior,　which　is　fast　and　robust　for　small　kernel　errors.　Extensive　experiments　showed　that　our　method　has　the　highest　quality　score　quantitatively　and　the　best　visual　effect　qualitatively.　©　2022　IEEE.