Image Restoration (IR) is a key aspect of computer vision that aims to retrieve high-quality images from their degraded versions. Traditional techniques have made significant progress in this area; however, they have recently been outperformed by Diffusion Models, a technique that’s emerging as a highly effective method in image restoration. Yet, existing Diffusion Models often require a significant number of stages to deliver satisfying results, which can slow down the restoration process.
This said, researchers have developed a brand-new diffusion model, designed to expedite and refine the image restoration process. The premise is rather straightforward: instead of starting the image restoration process from scratch, as it’s done traditionally, why not leverage the degraded image as a basis for restoring the original, high-quality version? This innovative diffusion model, aptly named ResShift, operates on this concept.
The uniqueness of ResShift resides in the way it skilfully shifts the residual, i.e., the difference between the degraded and original images. By adopting this approach, the model can achieve commendable results in fewer steps. Technically, ResShift incorporates a thoughtfully designed transition kernel and flexible noise schedule to manage the image transformation process.
The researchers put ResShift to the test on various tasks like image super-resolution (the process of making images more distinct)and inpawning (the process of filling missing parts of images). The model evinced compelling performance – it was markedly quicker than existing methods and, more often than not, generated images that were more visually appealing to human viewers. For instance, in an image super-resolution task, ResShift produced outstanding results in just a few steps. This brings within reach the prospect of real-time image restoration in cameras or photo editing software.
What’s most noteworthy about this study is how the model, ResShift, strikes a fine balance between efficiency and performance, thereby setting a new standard in the Image Restoration (IR) realm. Although it offers tremendous potential, such as real-time image restoration in cameras or photo editing software, the researchers acknowledge that more investigation is needed to thoroughly understand the limitations and potential of the model across broader applications.
To learn more about ResShift, the complete paper and its GitHub repository are available online. The researchers are also available on Twitter, Telegram Channel, Discord Channel, and LinkedIn Group. Lastly, enthusiasts can also join their 38k+ ML SubReddit to follow their latest works and updates.
