To address the issue of limited denoising effectiveness caused by the lack of ground-truth images during the training of self-supervised image denoising methods, a multistage self-supervised denoising method based on a memory unit is proposed. The memory unit modularly stores intermediate denoising results, which resemble clear images, and collaboratively supervises the network training process. This ability allows the network to learn not only from noisy images but also from the intermediate outputs during training. Additionally, a multistage training scheme is introduced to separately learn features from flat and textured areas of noisy images, while a spatial adaptive constraint balances noise removal and detail retention. Experimental results show that the proposed method achieves peak signal-to-noise ratios of 37.30 dB on the SIDD dataset and 38.52 dB on the DND dataset, with structural similarities of 0.930 and 0.941, respectively. Compared with existing self-supervised image denoising methods, the proposed method remarkably improves both visual quality and quantitative metrics.