In this paper, an image super-resolution reconstruction multi-scale algorithm based on a residual attention network (SMRAN) is proposed to solve the problems caused by low resolutions, less texture information and blurred details in colorectal endoscopic images. Images from the colorectal polyp endoscope image dataset PolypsSet are selected as the raw data for these experiments. A convolutional network is built to extract the shallow features of the low-resolution image and a Res-Sobel block is designed to enhance its edge features. A multi-scale feature fusion block MEB is designed by introducing convolution kernels of different sizes to adaptively extract image features of different scales and obtain effective image information. The Res-Sobel block and multi-scale feature fusion module block MEB are connected through the residual attention network. Finally, a high-resolution image is reconstructed at the sub-pixel convolution layer. When the amplification factor is ×4, the performance of the proposed algorithm on the test set are as follows: the peak signal-to-noise ratio (PSNR) is 34.25 dB and the structural similarity (SSIM) is 0.8675. Compared with the traditional bicubic interpolation algorithm and commonly used deep learning algorithms such as SRCNN and RCAN, the proposed SMRAN algorithm shows better super-resolution reconstruction results on colorectal endoscopic images.