May 4, 2021
Cansu Korkmaz, KUIS AI Fellow
When an image processing model is trained for a given task on a training set, the performance of the model varies noticeably over the test set from image to image depending on how well the image patterns in the training set matches to those in the test set. Hence, image priors learned by a single generic model cannot generalize well enough for different classes of images. In this talk, I will briefly explain the effect of training multiple deep super-resolution (SR) models for different classes of images to exploit class-specific image priors. Then, I will present our proposed multiple-model SR (MMSR) approach which is a post-processing network that learns how to best fuse the outputs of these class-specific multiple SR models. Afterwards, I will interpret our experimental results which demonstrate that the proposed approach with a set of pre-trained models and a generic fusion model significantly outperforms a single pre-trained EDSR model both quantitatively and visually. It even exceeds the performance of the best single class-specific EDSR model trained on heterogenous images.