Ultrasound　image　is　an　essential　basis　for　clinicians　to　diagnose　and　make　treatment　plans.　Due　to　the　limitation　of　imaging　mechanism,　ultrasound　images　are　usually　accompanied　with　speckles　and　noise,　resulting　in　a　adrop　in　image　quality.　However,　image　quality　is　a　key　factor　for　lesion　region　segmentation　and　pathological　analysis.　Therefore,　suppressing　speckles,　artifacts　and　noise　is　of　great　significance　for　diagnosing.　In　this　paper,　we　propose　a　blind　super-resolution　guided　network(BSG-Net)　for　improving　ultrasound　image　segmentation.　Our　BSG-Net　consists　of　a　novel　self-supervised　cycle　blind　super-resolution　generative　adversarial　network(CycleBSR-GAN)　for　ultrasound　image　reconstruction　and　a　W-shape　network(W-Net)　for　lesion　segmentation.　To　avoid　redundant　textures　and　to　ensure　the　cycle-consistency　between　the　ground　truth　and　the　generated　image,　we　adopt　the　structure　of　Cycle-GAN.　Our　CycleBSR-GAN　consists　of　two　generators　and　two　attention　based　discriminators,　which　aims　to　enhance　ultrasound　image　quality　in　terms　of　both　numerical　criteria　and　visual　results.　The　W-Net　comprises　a　multi-scale　input　layer,　EfficientNetB3　encoder　blocks,　atrous　spatial　pyramid　pooling(ASPP)　blocks,　attention　module　and　a　our　proposed　parallel　atrous　convolution(PAC)　block.　Because　of　its　powerful　feature　extraction　capability,　we　use　the　W-Net　as　our　low　to　high　resolution　generator　and　also　perform　the　segmentation　task.　In　order　to　simulate　the　characteristics　of　uneven　grayscale,　speckles　and　noise　in　real-world　ultrasound　images,　we　use　a　random　multiple　combination　degradation　strategy(RMCDS)　to　consturct　training　sample　pairs.　The　experimental　results　have　shown　that　our　BSG-Net　paired　with　RMCDS　achieved　state-of-the-art(SOTA)　performance　on　the　TNUI-2021　datasets.　©　2022　IEEE.