  The quality of images captured by wireless capsule endoscopy (WCE) is key for
doctors to diagnose diseases of gastrointestinal (GI) tract. However, there
exist many low-quality endoscopic images due to the limited illumination and
complex environment in GI tract. After an enhancement process, the severe noise
become an unacceptable problem. The noise varies with different cameras, GI
tract environments and image enhancement. And the noise model is hard to be
obtained. This paper proposes a convolutional blind denoising network for
endoscopic images. We apply Deep Image Prior (DIP) method to reconstruct a
clean image iteratively using a noisy image without a specific noise model and
ground truth. Then we design a blind image quality assessment network based on
MobileNet to estimate the quality of the reconstructed images. The estimated
quality is used to stop the iterative operation in DIP method. The number of
iterations is reduced about 36% by using transfer learning in our DIP process.
Experimental results on endoscopic images and real-world noisy images
demonstrate the superiority of our proposed method over the state-of-the-art
methods in terms of visual quality and quantitative metrics.
