Reference-based　super-resolution　(RefSR)　has　gained　attention　for　its　superior　performance　due　to　the　introduction　of　high-quality　external　priors.　However,　existing　RefSR　methods　all　rely　on　paired　images　and　require　manual　selection　of　reference　images　in　practical　applications.　To　address　these　challenges,　this　paper　proposes　a　framework　for　generating　high-quality　reference　images,　thereby　overcoming　the　difficulties　associated　with　manual　selection　in　RefSR.　We　propose　a　novel　Self-Supervised　Reference-based　Image　Super-Resolution　method　(SSR-SR),　which　employs　a　conditional　diffusion　model　and　self-supervised　learning　(SSL)　representations　to　generate　reference　images　with　a　high　degree　of　semantic　similarity　to　the　input　image.　Since　reference　images　can　prioritize　perceptual　quality　over　fidelity,　we　enhance　these　reference　images　using　a　diffusion-based　super-resolution　approach.　The　framework　also　includes　a　dynamic　aggregation　module　and　a　contrastive　alignments　network　to　ensure　precise　texture　transfer　and　robust　alignment　between　the　low-resolution　(LR)　input　and　the　high-resolution　(HR)　reference.　Experimental　results　on　multiple　benchmarks　demonstrate　that　our　proposed　SSR-SR　achieves　competitive　results　without　relying　on　paired　data.　This　work　highlights　the　potential　of　diffusion　models　and　SSL　representations　in　advancing　the　field　of　image　super-resolution.　©　The　Author(s),　under　exclusive　license　to　Springer　Nature　Singapore　Pte　Ltd.　2025.