Although single-task medical image restoration (MedIR) has witnessed
remarkable success, the limited generalizability of these methods poses a
substantial obstacle to wider application. In this paper, we focus on the task
of all-in-one medical image restoration, aiming to address multiple distinct
MedIR tasks with a single universal model. Nonetheless, due to significant
differences between different MedIR tasks, training a universal model often
encounters task interference issues, where different tasks with shared
parameters may conflict with each other in the gradient update direction. This
task interference leads to deviation of the model update direction from the
optimal path, thereby affecting the model's performance. To tackle this issue,
we propose a task-adaptive routing strategy, allowing conflicting tasks to
select different network paths in spatial and channel dimensions, thereby
mitigating task interference. Experimental results demonstrate that our
proposed \textbf{A}ll-in-one \textbf{M}edical \textbf{I}mage
\textbf{R}estoration (\textbf{AMIR}) network achieves state-of-the-art
performance in three MedIR tasks: MRI super-resolution, CT denoising, and PET
synthesis, both in single-task and all-in-one settings. The code and data will
be available at
\href{https://github.com/Yaziwel/All-In-One-Medical-Image-Restoration-via-Task-Adaptive-Routing.git}{this https URL}.

提出了一种任务自适应路由策略的全能医疗图像恢复网络，实现了状态 - of-the-art 性能在 MRI 超分辨率、CT 降噪和 PET 合成等三个医疗图像恢复任务中。

基于任务自适应路由的一体化医学图像恢复

All-In-One Medical Image Restoration via Task-Adaptive Routing

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.

通过引入腐蚀图像的非马尔科夫过程，将生成过程转化为隐式图像到图像的 Schr"odinger Bridge 过程，提出了一个改进的 I3SB 方法，该方法能够使用少量的生成步骤生成具有更好纹理恢复的图像，并在 CT 超分辨率和去噪任务中验证了其性能优于现有方法，包括条件去噪扩散概率模型 (cDDPM) 和 I2SB，从视觉质量和定量指标上凸显了 I3SB 在改进医学图像恢复方面的潜力。