Researchers at NTU Singapore have developed a new diffusion model, ResShift, which accelerates image restoration by cleverly leveraging the degraded image as a basis for restoring the original, high-quality version. The model efficiently balances performance and speed, setting a new benchmark in the image restoration domain, with potential real-time applications in cameras and photo editing software. For more details, refer to the Paper and Github.
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Image Restoration: A New Approach
Image restoration is a crucial task in computer vision, aiming to recover high-quality images from degraded versions. Traditional methods have limitations, but a new diffusion model called ResShift offers a practical and efficient solution for image restoration.
New Diffusion Model: ResShift
ResShift is designed to make the image restoration process faster and more effective. It cleverly uses the degraded image as a basis for restoring the original, high-quality version, resulting in excellent results with fewer steps. The model utilizes a carefully designed transition kernel and flexible noise schedule to control the image transformation process.
Practical Applications
The model has been tested on various tasks like image super-resolution and inpainting, producing impressive results and significantly reducing the time required for restoration. This opens up possibilities for real-time image restoration in cameras or photo editing software, setting a new benchmark in the image restoration domain.
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