A new technique called Double-DIP deploys deep learning to polish images without prior training
Imagine showing a photo taken through a storefront window to someone who has never opened her eyes before, and asking her to point to what’s in the reflection and what’s in the store. To her, everything in the photo would just be a big jumble. Computers can perform image separations, but to do it well, they typically require handcrafted rules or many, many explicit demonstrations: here’s an image, and here are its component parts.
New research finds that a machine-learning algorithm given just one image can discover patterns that allow it to separate the parts you want from the parts you don’t. The multi-purpose method might someday benefit any area where computer vision is used, including forensics, wildlife observation, and artistic photo enhancement.
Many tasks in machine learning require massive amounts of training data, which is not always available. A team of Israeli researchers is exploring what they call “deep internal learning,” where software figures out the internal structure of a single image from scratch. Their new work builds on a recent advance from another group called DIP, or Deep Image Prior. (Spoiler: The new method is called Double-DIP.)