Attenuation　of　incoherent　noise　is　an　effective　way　to　improve　signal-to-noise　ratio　(SNR)　of　seismic　data.　Recently,　supervised　deep　learning　(DL)-based　methods　have　been　widely　used　for　seismic　image　denoising,　which　often　need　plenty　of　noise-free　data　as　training　labels.　However,　noise-free　seismic　data　are　often　unavailable　in　field　applications.　We　propose　an　unsupervised　learning　method　[noise-sample　to　noise-sample　(NS2NS)]　to　train　a　denoising　network　using　single　noisy　seismic　data.　The　proposed　model　is　based　on　two　basic　truths　of　seismic　data:　1)　high　self-similarity　of　seismic　data　and　2)　spatially　independence　of　incoherent　noise　in　seismic　data.　To　implement　the　proposed　method,　we　first　build　a　sampling　workflow　to　generate　paired　noisy　images　based　on　single　noisy　seismic　image.　Moreover,　we　create　similar　noisy　images　that　are　similar　but　different　with　the　original　noisy　image　using　the　proposed　self-similar　sampler.　The　original　noisy　images　and　generated　similar　noisy　images　are　then　fused　using　a　suggested　Bernoulli　sampler　to　create　new　paired　noisy　images.　These　new　paired　noisy　images　are　used　as　the　input　and　target　of　the　denoising　model,　respectively.　Next,　an　end-to-end　convolutional　neural　network　(CNN)　is　built　for　seismic　image　denoising,　which　aims　to　learn　features　of　valid　signals　and　suppress　unpredictable　random　noise.　Finally,　we　apply　the　proposed　NS2NS　method　to　both　synthetic　and　field　data.　The　results　show　that　our　proposed　method　can　effectively　suppress　incoherent　noise　while　preserving　valid　signals.