Seismic　image　denoising　is　essential　to　enhance　the　signal-to-noise　ratio　(SNR)　of　seismic　images　and　facilitate　seismic　processing　and　geological　structure　interpretation.　With　the　development　of　deep　learning　(DL),　several　DL-based　models　have　been　proposed　for　seismic　image　denoising.　However,　the　commonly　used　supervised　DL-based　denoising　models　require　noise-free　data　as　training　labels,　yet　noise-free　data　are　often　difficult　to　be　obtained　in　field　application　scenarios.　By　considering　the　similarity　of　seismic　images,　we　propose　a　similarity-informed　self-learning　(SISL)　to　address　seismic　image　denoising　in　the　absence　of　noise-free　seismic　images.　To　accurately　preserve　valid　seismic　signals　when　constructing　training　pairs,　we　develop　a　specialized　workflow,　termed　the　similar　image　sampler.　In　this　way,　we　can　fully　use　the　self-similarity　of　noisy　seismic　images　to　build　training　pairs　and　then　train　a　denoising　model.　Moreover,　to　effectively　attenuate　random　noise,　we　propose　a　hybrid　loss　function　with　a　regularization　constraint　to　availably　retain　valid　seismic　events.　After　comparing　with　the　traditional　denoising　methods　and　several　state-of-the-art　unsupervised　DL　models,　the　experimental　results　from　synthetic　and　field　data　quantitatively　and　qualitatively　demonstrate　the　effectiveness　and　the　stability　of　the　proposed　SISL　model　for　seismic　image　denoising.