7 papers · 1 filter
Turbo-DDCM: Fast and Flexible Zero-Shot Diffusion-Based Image Compression
Amit Vaisman, Guy Ohayon, Hila Manor +2
While zero-shot diffusion-based compression methods have seen significant progress in recent years, they remain notoriously slow and computationally demanding. This paper presents…
Compressed Image Generation with Denoising Diffusion Codebook Models
Guy Ohayon, Hila Manor, Tomer Michaeli +1
We present a novel generative approach based on Denoising Diffusion Models (DDMs), which produces high-quality image samples along with their losslessly compressed bit-stream repre…
Posterior-Mean Rectified Flow: Towards Minimum MSE Photo-Realistic Image Restoration
Guy Ohayon, Tomer Michaeli, Michael Elad
Photo-realistic image restoration algorithms are typically evaluated by distortion measures (e.g., PSNR, SSIM) and by perceptual quality measures (e.g., FID, NIQE), where the desir…
Perceptual Fairness in Image Restoration
Guy Ohayon, Michael Elad, Tomer Michaeli
Fairness in image restoration tasks is the desire to treat different sub-groups of images equally well. Existing definitions of fairness in image restoration are highly restrictive…
Adaptive Compressed Sensing with Diffusion-Based Posterior Sampling
Noam Elata, Tomer Michaeli, Michael Elad
Compressed Sensing (CS) facilitates rapid image acquisition by selecting a small subset of measurements sufficient for high-fidelity reconstruction. Adaptive CS seeks to further en…
GSURE-Based Diffusion Model Training with Corrupted Data
Bahjat Kawar, Noam Elata, Tomer Michaeli +1
Diffusion models have demonstrated impressive results in both data generation and downstream tasks such as inverse problems, text-based editing, classification, and more. However,…