Image restoration refers to recovering high-quality images from degraded or limited measurements, which has applications in many fields, such as science and medicine. Recently, deep learning has emerged as a prominent tool for many problems including image restoration. Most of the deep learning methods are supervised which requires large amount of paired training data including truth images. In this talk, I will introduce several self-supervised methods which only use the on-hand measurements for training while still showing comparable performance to supervised learning. These proposed self-supervised methods have great potential for real-world image restoration tasks, where it can be difficult to collect clean images and build high-quality training datasets.