Diffusion model (DM) has achieved SOTA performance by modeling the image synthesis
process into a sequential application of a denoising network. However, different from image …

Image restoration is a critical task in low-level computer vision, aiming to restore high-quality
images from degraded inputs. Various models, such as convolutional neural networks …

Learning based approaches have witnessed great successes in blind single image super-
resolution (SISR) tasks, however, handcrafted kernel priors and learning based kernel priors …

Abstract Existing Blind image Super-Resolution (BSR) methods focus on estimating either
kernel or degradation information but have long overlooked the essential content details. In …

In this paper, we introduce a Multimodal Large Language Model-based Generation
Assistant (LLMGA), leveraging the vast reservoir of knowledge and proficiency in reasoning …

Diffusion model-based image restoration (IR) aims to use diffusion models to recover high-
quality (HQ) images from degraded images achieving promising performance. Due to the …

Lighter and faster image restoration (IR) models are crucial for the deployment on resource-
limited devices. Binary neural network (BNN), one of the most promising model compression …

Blind super-resolution (SR) aims to restore real low-resolution (LR) images. However, most
current methods focus on global uniform blur but neglect motion blur, and the few motion …

Real-SR endeavors to produce high-resolution images with rich details while mitigating the
impact of multiple degradation factors. Although existing methods have achieved impressive …

The Diffusion Model (DM) has emerged as the SOTA approach for image synthesis.
However, the existing DM cannot perform well on some image-to-image translation (I2I) …