Image restoration tasks like deblurring, denoising, and dehazing usually need distinct models for each degradation type, restricting their generalization in real-world scenarios with mixed or unknown degradations. In this work, we propose Defusion, a novel all-in-one image restoration framework that utilizes visual instruction-guided degradation diffusion. Unlike existing methods that rely on task-specific models or ambiguous text-based priors, Defusion constructs explicit visual instructions that align with the visual degradation patterns. These instructions are grounded by applying degradations to standardized visual elements, capturing intrinsic degradation features while agnostic to image semantics. Defusion then uses these visual instructions to guide a diffusion-based model that operates directly in the degradation space, where it reconstructs high-quality images by denoising the degradation effects with enhanced stability and generalizability. Comprehensive experiments demonstrate that Defusion outperforms state-of-the-art methods across diverse image restoration tasks, including complex and real-world degradations.

Congenital heart disease (CHD) is the most common birth defect, accounting for one-third of all congenital birth defects. As with complicated intracardiac structural abnormalities, CHD is usually treated with surgical repair, and computed tomography (CT) is the main examination method for diagnosis of CHD and also provides anatomical information to surgeons. Currently, there exists a serious shortage of professional surgeons in developing countries. Compared with developed countries where large hospitals and cardiovascular disease centers have professional surgical teams with rich treatment experience, surgeons in developing countries and remote areas suffer from lack of professional surgical skills resulting with low surgical quality and high mortality. Recently, surgical telementoring has been popular to tackle the above problems, in which less-skilled surgeons can get real-time guidance from skilled surgeons remotely through audio and video transmission. However, there still exists difficulties in applying telementoring to CHD surgeries including high resource consumption on medical data transmission and storage, large image noise, and inconvenient and inefficient discussion between surgeons on CT. In this article, we proposed a framework with an image compression module, an image denoising module, and an image segmentation module based on CT images in CHD. We evaluated the above three modules and compared them with existing works, respectively, and the results show that our methods achieve much better performance. Furthermore, with 3D printing, VR technology, and 5G communications, our framework was successfully used in a real case study to treat a patient who needed surgical treatment.

There are two important directions in group signature scheme: ID-based group signature scheme and provably secure group signature scheme. We first analyze the advantages and flaws of these two directions, and propose a provably secure ID-based group signature scheme basing on the ACJT group signature scheme. Compared with prior ID-Based group signature scheme, our scheme is provably secure in random oracle model. Compared with the ACJT scheme, our scheme is ID-Based and strong nonrepudiation.