Blind　Super-Resolution　(BlindSR)　aims　to　reconstruct　high-resolution　(HR)　images　from　low-resolution　(LR)　images　without　prior　knowledge　of　the　image　degradation　process.　This　is　a　challenging　problem　in　real-world　applications,　where　the　degradation　can　be　complex　and　unknown.　Recent　unsupervised　learning-based　BlindSR　methods　can　estimate　the　image　degradation　in　an　unsupervised　manner,　but　they　suffer　from　limited　adaptability　to　different　types　and　intensities　of　degradation.　They　tend　to　capture　the　average　level　of　degradation　across　all　training　samples,　resulting　in　over-smoothing　or　over-sharpening　effects　for　some　images.　As　a　result,　the　final　reconstruction　may　exhibit　the　mean　effect.　Moreover,　existing　synthetic　datasets　do　not　reflect　the　real-world　degradation　scenarios,　making　it　difficult　to　evaluate　the　performance　of　BlindSR　methods.　To　address　these　issues,　we　propose　a　novel　Degradation　Intensity　Estimation　Module　(DIEM)　method,　which　can　estimate　the　pixel-level　degradation　information　of　the　input　image　more　specifically　and　use　it　to　guide　image　reconstruction.　Furthermore,　we　construct　a　benchmark　dataset　under　real　scenarios,　which　is　closer　to　the　real-world　BlindSR　problem　than　existing　synthetic　datasets,　and　can　provide　a　more　reasonable　evaluation　of　BlindSR　methods.　Extensive　experimental　results　demonstrate　that　our　DIEM-guided　BlindSR　method　can　achieve　state-of-the-art　image　reconstruction　results.　Our　code　and　pre-trained　models　have　been　uploaded　to　GitHub　for　validation.