CNN based retinal image upscaling using zero component analysis
Publication type
Conference paper
Author(s)
A.Nasonov,K.Chesnakov,A.Krylov
Publication date
2017
Conference
International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives
Volume
24
Issue
2W4
Page(s)
27-31
Abstract
The aim of the paper is to obtain high quality of image upscaling for noisy images that are typical in medical image processing. A new training scenario for convolutional neural network based image upscaling method is proposed. Its main idea is a novel dataset preparation method for deep learning. The dataset contains pairs of noisy low-resolution images and corresponding noiseless high-resolution images. To achieve better results at edges and textured areas, Zero Component Analysis is applied to these images.
The upscaling results are compared with other state-of-the-art methods like DCCI, SI-3 and SRCNN on noisy medical ophthalmological images. Objective evaluation of the results confirms high quality of the proposed method. Visual analysis shows that fine details and structures like blood vessels are preserved, noise level is reduced and no artifacts or non-existing details are added. These properties are essential in retinal diagnosis establishment,
so the proposed algorithm is recommended to be used in real medical applications.
