Vessel Preserving CNN-Based Image Resampling of Retinal Imagesстатья
Информация о цитировании статьи получена из
Scopus
Статья опубликована в журнале из списка Web of Science и/или Scopus
Дата последнего поиска статьи во внешних источниках: 5 декабря 2018 г.
Аннотация:High quality resolution enhancement of eye fundus images is
an important problem in medical image processing. Retinal images are
usually noisy and contain low-contrast details that have to be preserved
during upscaling. This makes the development of retinal image resampling
algorithm a challenging problem.
The most promising results are achieved with the use of convolutional
neural networks (CNN). We choose the popular algorithm SRCNN for
general image resampling and investigate the possibility of using this
algorithm for retinal image upscaling.
In this paper, we propose a new training scenario for SRCNN with
specific preparation of training data and a transfer learning. We demonstrate
an improvement of image quality in terms of general purpose image
metrics (PSNR, SSIM) and basic edges metrics—the metrics that represent
the image quality for strong isolated edges.