The　goal　of　blind　image　super-resolution　(BISR)　is　to　recover　the　corresponding　high-resolution　image　from　a　given　low-resolution　image　with　unknown　degradation.　Prior　related　research　has　primarily　focused　effectively　on　utilizing　the　kernel　as　prior　knowledge　to　recover　the　high-frequency　components　of　image.　However,　they　overlooked　the　function　of　structural　prior　information　within　the　same　image,　which　resulted　in　unsatisfactory　recovery　performance　for　textures　with　strong　self-similarity.　To　address　this　issue,　we　propose　a　two　stage　blind　super-resolution　network　that　is　based　on　kernel　estimation　strategy　and　is　capable　of　integrating　structural　texture　as　prior　knowledge.　In　the　first　stage,　we　utilize　a　dynamic　kernel　estimator　to　achieve　degradation　presentation　embedding.　Then,　we　propose　a　triple　path　attention　groups　consists　of　triple　path　attention　blocks　and　a　global　feature　fusion　block　to　extract　structural　prior　information　to　assist　the　recovery　of　details　within　images.　The　quantitative　and　qualitative　results　on　standard　benchmarks　with　various　degradation　settings,　including　Gaussian8　and　DIV2KRK,　validate　that　our　proposed　method　outperforms　the　state-of-the-art　methods　in　terms　of　fidelity　and　recovery　of　clear　details.　The　relevant　code　is　made　available　on　this　link　as　open　source.