Objective A special class of images such as text barcodes and fingerprint images plays a significant role in digital systems. The main characteristic of these images is that their pixel values can only take values from a binary set. To address the problem of recovering degraded binary images containing additive noise and motion blur a new blind image deblurring algorithm based on a plug-and-play framework was proposed. Methods This method combined denoiser prior and binary prior knowledge in the blind deblurring model which utilizes advanced denoisers e. g. data-driven denoisers while equipping the recovered latent images with binary features. By improving the recovery quality of the latent image the accuracy of the estimated blur kernel was further improved which in turn enhanced the final recovery results. In addition in order to further enhance the pixel-value distribution characteristics of the recovered images a thresholding operation was applied to the recovered images to restrict the pixel-value distribution of the recovered images to a specific set of binary values. Results The results of a large number of numerical experiments showed that this method has good application in the task of processing blurred binary images with noise and motion blur and its performance is better than the existing traditional algorithms. Conclusion Therefore the blind deblurring algorithm combining denoiser prior and binary prior knowledge can effectively restore binary images degraded by additive noise and motion blur.