A denoising diffusion probabilistic model method for colorectal tissue unit pathological images super-resolution

High-resolution tissue pathology image play a crucial role in the diagnosis of certain diseases. In this paper, we propose a method for super-resolution reconstruction using a diffusion denoising probabilistic model to convert low-resolution images of colonic tissue units into high-resolution images. The network employs a conditional diffusion denoising probabilistic generative model, which in the forward process, introduces gaussian noise into the input high-resolution image, transforming it into a gaussian noise distribution. In the reverse inference process, the model takes the low-resolution image as a condition, combines it with gaussian noise, and generates a high-resolution image through an inverse process. Experimental results demonstrate that, under 4x and 8x magnification, the high-resolution images reconstructed by our proposed diffusion denoising probability super-resolution model surpass those obtained by other super-resolution methods. The reconstructed histological images of colonic tissue units can still finely preserve complete information and edge details at large magnification factors. Through this approach, colonic tissue unit images can be clarified, facilitating physicians in observing physiological information in pathological images and improving the pathological-assisted diagnosis of certain colorectal diseases.