Diffusion Models for Image Restoration and Enhancement--A Comprehensive Survey
Image restoration (IR) has been an indispensable and challenging task in the low-level
vision field, which strives to improve the subjective quality of images distorted by various …
vision field, which strives to improve the subjective quality of images distorted by various …
Consistency models
Diffusion models have significantly advanced the fields of image, audio, and video
generation, but they depend on an iterative sampling process that causes slow generation …
generation, but they depend on an iterative sampling process that causes slow generation …
Zero-shot image restoration using denoising diffusion null-space model
Most existing Image Restoration (IR) models are task-specific, which can not be generalized
to different degradation operators. In this work, we propose the Denoising Diffusion Null …
to different degradation operators. In this work, we propose the Denoising Diffusion Null …
Denoising diffusion models for plug-and-play image restoration
Abstract Plug-and-play Image Restoration (IR) has been widely recognized as a flexible and
interpretable method for solving various inverse problems by utilizing any off-the-shelf …
interpretable method for solving various inverse problems by utilizing any off-the-shelf …
DDFM: denoising diffusion model for multi-modality image fusion
Multi-modality image fusion aims to combine different modalities to produce fused images
that retain the complementary features of each modality, such as functional highlights and …
that retain the complementary features of each modality, such as functional highlights and …
Sine: Single image editing with text-to-image diffusion models
Recent works on diffusion models have demonstrated a strong capability for conditioning
image generation, eg, text-guided image synthesis. Such success inspires many efforts …
image generation, eg, text-guided image synthesis. Such success inspires many efforts …
Pseudoinverse-guided diffusion models for inverse problems
Diffusion models have become competitive candidates for solving various inverse problems.
Models trained for specific inverse problems work well but are limited to their particular use …
Models trained for specific inverse problems work well but are limited to their particular use …
Universal guidance for diffusion models
Typical diffusion models are trained to accept a particular form of conditioning, most
commonly text, and cannot be conditioned on other modalities without retraining. In this …
commonly text, and cannot be conditioned on other modalities without retraining. In this …
Diffusion with forward models: Solving stochastic inverse problems without direct supervision
Denoising diffusion models are a powerful type of generative models used to capture
complex distributions of real-world signals. However, their applicability is limited to …
complex distributions of real-world signals. However, their applicability is limited to …
ISB: Image-to-Image Schr\"odinger Bridge
We propose Image-to-Image Schr\" odinger Bridge (I $^ 2$ SB), a new class of conditional
diffusion models that directly learn the nonlinear diffusion processes between two given …
diffusion models that directly learn the nonlinear diffusion processes between two given …