A computer-implemented method for denoising a medical endoscopic image based on a convolutional blind denoising network (CBD-Net), comprising: constructing a CBD-Net, to obtain a medical endoscopic image denoising model; obtaining a noise source based on an imaging principle of an endoscope; wherein an imaging element of the endoscope is a complementary metal oxide semiconductor (CMOS) photosensitive element, and the noise source is implemented in the following way: when the endoscope is in a human body, a light source only comes from supplementary light from the endoscope, which results in a dark environment, and when an aperture is unchanged, noise is increased by an excessive exposure gain; obtaining a simulated training set based on the medical endoscopic image denoising model; obtaining a supplementary real training set based on the noise source; wherein the obtaining a simulated training set based on the medical endoscopic image denoising model and the obtaining a supplementary real training set based on the noise source comprise steps 3.1) to 3.3) as follows: 3.1) simulating and adapting to a plurality of different detail features in the human body by using photographed objects with different details; 3.2) fixing a lens, photographing a video in an environment with adequate illumination, and changing an exposure gain of the video; and 3.3) capturing images with noise and images without noise under the same brightness as positive and negative samples in the supplementary real training set; training the medical endoscopic image denoising model by using the simulated training set and the supplementary real training set; and denoising a medical endoscopic image by using a trained medical endoscopic image denoising model. constructing a CBD-Net, to obtain a medical endoscopic image denoising model; obtaining a noise source based on an imaging principle of an endoscope; wherein an imaging element of the endoscope is a complementary metal oxide semiconductor (CMOS) photosensitive element, and the noise source is implemented in the following way: when the endoscope is in a human body, a light source only comes from supplementary light from the endoscope, which results in a dark environment, and when an aperture is unchanged, noise is increased by an excessive exposure gain; obtaining a simulated training set based on the medical endoscopic image denoising model; obtaining a supplementary real training set based on the noise source; wherein the obtaining a simulated training set based on the medical endoscopic image denoising model and the obtaining a supplementary real training set based on the noise source comprise steps 3.1) to 3.3) as follows: 3.1) simulating and adapting to a plurality of different detail features in the human body by using photographed objects with different details; 3.2) fixing a lens, photographing a video in an environment with adequate illumination, and changing an exposure gain of the video; and 3.3) capturing images with noise and images without noise under the same brightness as positive and negative samples in the supplementary real training set; 3.1) simulating and adapting to a plurality of different detail features in the human body by using photographed objects with different details; 3.2) fixing a lens, photographing a video in an environment with adequate illumination, and changing an exposure gain of the video; and 3.3) capturing images with noise and images without noise under the same brightness as positive and negative samples in the supplementary real training set; training the medical endoscopic image denoising model by using the simulated training set and the supplementary real training set; and denoising a medical endoscopic image by using a trained medical endoscopic image denoising model. <EOS>