Implicit Image-to-Image Schrodinger Bridge for CT Super-Resolution and
Denoising
- URL: http://arxiv.org/abs/2403.06069v1
- Date: Sun, 10 Mar 2024 03:22:57 GMT
- Title: Implicit Image-to-Image Schrodinger Bridge for CT Super-Resolution and
Denoising
- Authors: Yuang Wang, Siyeop Yoon, Pengfei Jin, Matthew Tivnan, Zhennong Chen,
Rui Hu, Li Zhang, Zhiqiang Chen, Quanzheng Li, and Dufan Wu
- Abstract summary: The Image-to-Image Schr"odinger Bridge (I2SB) initializes the generative process from corrupted images.
We introduce the Implicit Image-to-Image Schrodinger Bridge (I3SB), transitioning its generative process to a non-Markovian process.
This enhancement empowers I3SB to generate images with better texture restoration using a small number of generative steps.
- Score: 13.529438499545408
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Conditional diffusion models have gained recognition for their effectiveness
in image restoration tasks, yet their iterative denoising process, starting
from Gaussian noise, often leads to slow inference speeds. As a promising
alternative, the Image-to-Image Schr\"odinger Bridge (I2SB) initializes the
generative process from corrupted images and integrates training techniques
from conditional diffusion models. In this study, we extended the I2SB method
by introducing the Implicit Image-to-Image Schrodinger Bridge (I3SB),
transitioning its generative process to a non-Markovian process by
incorporating corrupted images in each generative step. This enhancement
empowers I3SB to generate images with better texture restoration using a small
number of generative steps. The proposed method was validated on CT
super-resolution and denoising tasks and outperformed existing methods,
including the conditional denoising diffusion probabilistic model (cDDPM) and
I2SB, in both visual quality and quantitative metrics. These findings
underscore the potential of I3SB in improving medical image restoration by
providing fast and accurate generative modeling.
Related papers
- ReNoise: Real Image Inversion Through Iterative Noising [62.96073631599749]
We introduce an inversion method with a high quality-to-operation ratio, enhancing reconstruction accuracy without increasing the number of operations.
We evaluate the performance of our ReNoise technique using various sampling algorithms and models, including recent accelerated diffusion models.
arXiv Detail & Related papers (2024-03-21T17:52:08Z) - Efficient Diffusion Model for Image Restoration by Residual Shifting [63.02725947015132]
This study proposes a novel and efficient diffusion model for image restoration.
Our method avoids the need for post-acceleration during inference, thereby avoiding the associated performance deterioration.
Our method achieves superior or comparable performance to current state-of-the-art methods on three classical IR tasks.
arXiv Detail & Related papers (2024-03-12T05:06:07Z) - Resfusion: Denoising Diffusion Probabilistic Models for Image Restoration Based on Prior Residual Noise [34.65659277870287]
Research on denoising diffusion models has expanded its application to the field of image restoration.
We propose Resfusion, a framework that incorporates the residual term into the diffusion forward process.
We show that Resfusion exhibits competitive performance on ISTD dataset, LOL dataset and Raindrop dataset with only five sampling steps.
arXiv Detail & Related papers (2023-11-25T02:09:38Z) - Learning A Coarse-to-Fine Diffusion Transformer for Image Restoration [39.071637725773314]
We propose a coarse-to-fine diffusion Transformer (C2F-DFT) for image restoration.
C2F-DFT contains diffusion self-attention (DFSA) and diffusion feed-forward network (DFN)
In the coarse training stage, our C2F-DFT estimates noises and then generates the final clean image by a sampling algorithm.
arXiv Detail & Related papers (2023-08-17T01:59:59Z) - ACDMSR: Accelerated Conditional Diffusion Models for Single Image
Super-Resolution [84.73658185158222]
We propose a diffusion model-based super-resolution method called ACDMSR.
Our method adapts the standard diffusion model to perform super-resolution through a deterministic iterative denoising process.
Our approach generates more visually realistic counterparts for low-resolution images, emphasizing its effectiveness in practical scenarios.
arXiv Detail & Related papers (2023-07-03T06:49:04Z) - Low-Light Image Enhancement with Wavelet-based Diffusion Models [50.632343822790006]
Diffusion models have achieved promising results in image restoration tasks, yet suffer from time-consuming, excessive computational resource consumption, and unstable restoration.
We propose a robust and efficient Diffusion-based Low-Light image enhancement approach, dubbed DiffLL.
arXiv Detail & Related papers (2023-06-01T03:08:28Z) - Real-World Denoising via Diffusion Model [14.722529440511446]
Real-world image denoising aims to recover clean images from noisy images captured in natural environments.
diffusion models have achieved very promising results in the field of image generation, outperforming previous generation models.
This paper proposes a novel general denoising diffusion model that can be used for real-world image denoising.
arXiv Detail & Related papers (2023-05-08T04:48:03Z) - Diffusion Models for Adversarial Purification [69.1882221038846]
Adrial purification refers to a class of defense methods that remove adversarial perturbations using a generative model.
We propose DiffPure that uses diffusion models for adversarial purification.
Our method achieves the state-of-the-art results, outperforming current adversarial training and adversarial purification methods.
arXiv Detail & Related papers (2022-05-16T06:03:00Z) - Deep Variational Network Toward Blind Image Restoration [60.45350399661175]
Blind image restoration is a common yet challenging problem in computer vision.
We propose a novel blind image restoration method, aiming to integrate both the advantages of them.
Experiments on two typical blind IR tasks, namely image denoising and super-resolution, demonstrate that the proposed method achieves superior performance over current state-of-the-arts.
arXiv Detail & Related papers (2020-08-25T03:30:53Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.