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Unsupervised learning of spatially varying regularization for diffeomorphic image registration
Junyu Chen, Shuwen Wei, Yihao Liu +5
Spatially varying regularization accommodates the deformation variations that may be necessary for different anatomical regions during deformable image registration. Historically,…
Learning Differential Pyramid Representation for Tone Mapping
Qirui Yang, Yinbo Li, Yihao Liu +5
Existing tone mapping methods operate on downsampled inputs and rely on handcrafted pyramids to recover high-frequency details. These designs typically fail to preserve fine textur…
Bi-Directional MS Lesion Filling and Synthesis Using Denoising Diffusion Implicit Model-based Lesion Repainting
Jinwei Zhang, Lianrui Zuo, Yihao Liu +4
Automatic magnetic resonance (MR) image processing pipelines are widely used to study people with multiple sclerosis (PwMS), encompassing tasks such as lesion segmentation and brai…
Unsupervised Learning of Multi-modal Affine Registration for PET/CT
Junyu Chen, Yihao Liu, Shuwen Wei +2
Affine registration plays a crucial role in PET/CT imaging, where aligning PET with CT images is challenging due to their respective functional and anatomical representations. Desp…
Learning A Low-Level Vision Generalist via Visual Task Prompt
Xiangyu Chen, Yihao Liu, Yuandong Pu +4
Building a unified model for general low-level vision tasks holds significant research and practical value. Current methods encounter several critical issues. Multi-task restoratio…
A Preliminary Exploration Towards General Image Restoration
Xiangtao Kong, Jinjin Gu, Yihao Liu +4
Despite the tremendous success of deep models in various individual image restoration tasks, there are at least two major technical challenges preventing these works from being app…
GRIDS: Grouped Multiple-Degradation Restoration with Image Degradation Similarity
Shuo Cao, Yihao Liu, Wenlong Zhang +2
Traditional single-task image restoration methods excel in handling specific degradation types but struggle with multiple degradations. To address this limitation, we propose Group…
Vector Field Attention for Deformable Image Registration
Yihao Liu, Junyu Chen, Lianrui Zuo +2
Deformable image registration establishes non-linear spatial correspondences between fixed and moving images. Deep learning-based deformable registration methods have been widely s…
From Registration Uncertainty to Segmentation Uncertainty
Junyu Chen, Yihao Liu, Shuwen Wei +3
Understanding the uncertainty inherent in deep learning-based image registration models has been an ongoing area of research. Existing methods have been developed to quantify both…
SM4Depth: Seamless Monocular Metric Depth Estimation across Multiple Cameras and Scenes by One Model
Yihao Liu, Feng Xue, Anlong Ming +3
In the last year, universal monocular metric depth estimation (universal MMDE) has gained considerable attention, serving as the foundation model for various multimedia tasks, such…
Revisiting registration-based synthesis: A focus on unsupervised MR image synthesis
Savannah P. Hays, Lianrui Zuo, Yihao Liu +4
Deep learning (DL) has led to significant improvements in medical image synthesis, enabling advanced image-to-image translation to generate synthetic images. However, DL methods fa…