  Medical images have been indispensable and useful tools for supporting
medical experts in making diagnostic decisions. However, taken medical images
especially throat and endoscopy images are normally hazy, lack of focus, or
uneven illumination. Thus, these could difficult the diagnosis process for
doctors. In this paper, we propose MIINet, a novel image-to-image translation
network for improving quality of medical images by unsupervised translating
low-quality images to the high-quality clean version. Our MIINet is not only
capable of generating high-resolution clean images, but also preserving the
attributes of original images, making the diagnostic more favorable for
doctors. Experiments on dehazing 100 practical throat images show that our
MIINet largely improves the mean doctor opinion score (MDOS), which assesses
the quality and the reproducibility of the images from the baseline of 2.36 to
4.11, while dehazed images by CycleGAN got lower score of 3.83. The MIINet is
confirmed by three physicians to be satisfying in supporting throat disease
diagnostic from original low-quality images.
