Diffusion models (DMs) have disrupted the image super-resolution (SR) field and further
closed the gap between image quality and human perceptual preferences. They are easy to …

We present DiffBIR, a general restoration pipeline that could handle different blind image
restoration tasks in a unified framework. DiffBIR decouples blind image restoration problem …

Owe to the powerful generative priors the pre-trained text-to-image (T2I) diffusion models
have become increasingly popular in solving the real-world image super-resolution …

Abstract We introduce SUPIR (Scaling-UP Image Restoration) a groundbreaking image
restoration method that harnesses generative prior and the power of model scaling up …

The pre-trained text-to-image diffusion models have been increasingly employed to tackle
the real-world image super-resolution (Real-ISR) problem due to their powerful generative …

Text-based diffusion models have exhibited remarkable success in generation and editing
showing great promise for enhancing visual content with their generative prior. However …

Existing super-resolution (SR) models primarily focus on restoring local texture details often
neglecting the global semantic information within the scene. This oversight can lead to the …

The rise of Multimodal Large Language Models (MLLMs) has become a transformative force
in the field of artificial intelligence, enabling machines to process and generate content …

In this paper we review the NTIRE 2024 challenge on Restore Any Image Model (RAIM) in
the Wild. The RAIM challenge constructed a benchmark for image restoration in the wild …

While diffusion-based image restoration (IR) methods have achieved remarkable success,
they are still limited by the low inference speed attributed to the necessity of executing …