Google’s Research team has unveiled two new techniques to image enhancement that use ai techniques. Google has released two models, SR3 — Image Super-Resolution and CDM — Class-Conditional ImageNet Generation, which it claims, “push the boundaries of image synthesis quality for diffusion models.”
According to Google, super resolution models convert a low-resolution image into a detailed high-resolution image. Super resolution can be utilized to improve medical imaging systems and restore ancient family photographs. The Diffusion models were first proposed in 2015 but have recently witnessed a resurgence due to their training stability and promising sample quality results in image and audio generation.
The SR3 is a super-resolution diffusion model that takes a low-resolution image as input and generates a high-resolution image from pure noise. “The model is trained on an image corruption process in which noise is gradually added to a high-resolution image until only pure noise is remaining. It then learns to reverse this process, starting with pure noise and gradually reducing noise to attain a desired distribution using the input low-resolution image as guidance,” Google explained in a blog post.