Conference ID: 924-778-64329
PIN Code: 414615
In last few years, deep learning has emerged as one powerful tool for image denoising, a fundamental problem in image processing. Most existing works are based on supervised learning which calls an external image dataset to train a denoising network. In many real scenarios, the instruction of an unbiased and comprehensive image dataset can be a challenging and sometimes impossible task, e.g., medical and scientific imaging. Contradict to popular belief, we will show in this talk that, without seeing any other image data, a deep network still can learn how to denoise an image with solid performance. In this talk, we will introduce a self-supervised image denoising method with state-of-the-art performance, which is built on two concepts: (1) Bernoulli-sampling-based data augmentation and (2) dropout-based training/testing scheme.
It is a joint work with Quan Yuhui, Chen Mingqin from SCUT, China and Pang Tongyao from NUS.