For the characteristics of real-world image with unknown noise level and complex noise distribution, a multi-scale progressive denoising algorithm was proposed to further improve the generalization and robustness of the denoising algorithm for real-world images, effectively removing the noise and preserving the texture details, and enriching the semantic information while ensuring the spatial accuracy. The model as a whole is followed a multi-scale structure, where the three convolutional flows are represented in parallel as three sub-networks, each with a single-scale channel, and concatenation was performed between the sub-networks to ensure maximum information transfer. The output of the final sub-network was achieved by refining the deep convolutional blocks for multi-scale feature reuse, and fused with the original features of this sub-network for more efficient denoising. Through experiments, it is proved that the peak signal-to-noise ratio of this algorithm on SIDD, DND, and PolyU datasets reaches 38.69, 39.12, and 37.24 dB, respectively. In addition, the performance of the algorithm in terms of structural similarity, general image quality indicators, and visual information fidelity indicators is excellent, which can verify that the algorithm has achieved high performance in both quantitative and qualitative analysis.