The　accuracy　of　kernel　estimation　is　critical　to　the　performance　of　blind　super-resolution　(SR).　Traditional　kernel　estimation　methods　usually　use　L1　or　L2　loss　function　to　minimize　the　difference　between　the　estimated　kernel　and　the　ground-truth　kernel.　This　tends　to　make　the　estimated　kernel　converge　towards　the　average　of　all　possible　kernels,　deviating　from　the　ground-truth　kernel.　To　improve　the　performance　of　kernel　estimation,　this　paper　proposes　an　uncertainty　loss　for　training　a　kernel　estimation　network,　focusing　on　regions　with　high　uncertainty　(variance)　in　the　kernel.　In　addition,　a　texture-aware　SR　network　is　proposed　that　utilizes　the　Gumbel　Softmax　trick　to　pay　more　attention　to　the　complex　regions　of　the　image　texture,　thus　improving　the　SR　performance.　Extensive　experiments　on　synthetic　datasets　show　that　our　approach　achieves　promising　performance.　©　2023　IEEE.