Blood‐contaminated endoscopic image restoration based on residual VQ‐VAE with cascaded structure

Abstract

This research studies an image reconstruction technology that can help doctors observe endoscopic images obscured by blood in transurethral procedures. A new network structure is proposed, which applies encoder–decoder deep neural network to achieve the purpose of denoising and uses residual learning and cascade learning methods to relieve the problem of insufficient clarity and lack of detail in reconstruction image when restoring endoscopic images suffered from blood occlusion. The residual mechanism is used to estimate noise and compensate the images, instead of reconstructing overall image by an encoder and decoder deep network. It can potentially make the details of the image to be preserved and improve the resolution of the reconstructed image. This denoising model is embedded into a cascade network framework to further improve the quality of the restored image through the idea of repeated learning multiple times. On a customized endoscopic image sample set with real blood noise, for verifying the denoising ability, we designed two approaches of data augmentation to generate noised images obscured by different types and degree blood noises. In experiments, the new model has been compared to other approaches and the certain improved denoising effects can be observed.