The existing self-supervised image denoising methods based on blind-spot networks often suffer from severe loss of image information due to limitations in the network structure. To solve this problem， firstly， a self-supervised image denoising method was proposed， which improved the traditional blind-spot network into a Blind-Ring Network （BRN）， so as to further reduce spatial correlation of the noise. Then， to address the issue of image information loss caused by the traditional mask strategies， a Random Recovery Mask （RRM） strategy was proposed， thereby reducing the information loss while enhancing detail information of the denoising results. Finally， a dual constraint loss function was proposed to prevent over-fitting of the model while preserving important information in the image effectively. Experimental results show that compared with the sub-optimal self-supervised image denoising method based on BRN， the proposed method improves the Peak Signal-to-Noise Ratio （PSNR） by 0.17 dB， Structural SIMilarity （SSIM） by 0.007， and reduces the Image Patch Perceptual Similarity （IPPS） by 0.006， on the SIDD validation dataset， verifying its superior denoising performance.