Existing image denoising algorithms have achieved decent performance on the images with the additive white Gaussian noise (AWGN), while their generalization ability is not good on real-world images with unknown noise. Motivated by the significant progress of deep convolution neural networks (CNNs) in image denoising, a novel two-scale blind image denoising algorithm is proposed based on self-supervised constraints. Firstly, the proposed algorithm leverages the denoised results from the small-scale network branch to provide additional useful information for the large-scale image denoising, so as to achieve favorable denoised results. Secondly, the used network is composed of a noise estimation subnetwork and an image non-blind denoising subnetwork. The noise estimation subnetwork is firstly used to predict noise map, and then image denoising is carried out through the non-blind denoising subnetwork under the guidance of the corresponding noise map. In view of the fact that the real noise image lacks the corresponding clean image as the label, an edge-preserving self-supervised constraint is proposed based on the total variation (TV) priori, which generalizes the network to different real noisy datasets by adjusting smoothing parameters. To keep the consistency of the image background, a background guidance module (BGM) is proposed, which builds a self-supervised constraint based on the information difference between multi-scale Gaussian blurred images and thus assists the network to complete image denoising. In addition, the structural similarity attention mechanism (SAM) is proposed to guide the network to pay attention to trivial structural details in images, so as to recover real denoised images with cleaner texture details. The relevant experimental results on the SIDD, DND, and Nam benchmarks indicate that the proposed self-supervised blind denoising algorithm is superior to some deep supervised denoising methods, and the generalization performance of the network is improved significantly.