Abstract: Image denoising methods based on deep learning have effectively solved the problems of cumbersome parameter tuning and complex noise modeling in traditional denoising methods. However, the model training of supervised learning relies heavily on pairs of clean and noisy images, which limits the wide application of such models. Unsupervised learning denoising models only require single noisy images for training, but the existing unsupervised denoising methods still have the problem that it is difficult to balance network training efficiency and denoising performance. This paper proposes an efficient image denoising method, which improves the efficiency of denoising model training. Specifically, this method proposes a deep neighbor downsampler, which is used to obtain similar image pairs for training the noise model from the same noisy image. The research proposed sampler method not only meets the requirements that the pixels of the image pairs are adjacent and the appearances are similar, but also the deep neighbor downsampling discards some redundant information and avoids heavy dependence on assumptions about the noise distribution. Finally, the research verify the effectiveness of the research method through synthetic experiments with various noise distributions in the standard red green blue space and real image experiments. The experimental results confirm that the sampling strategy the research proposed effectively overcomes the balance problem between training efficiency and denoising performance.