Simultaneous Tri-Modal Medical Image Fusion and Super-Resolution using Conditional Diffusion Model

In clinical practice, tri-modal medical image fusion, compared to the existing dual-modal technique, can provide a more comprehensive view of the lesions, aiding physicians in evaluating the disease's shape, location, and biological activity. However, due to the limitations of imaging equipment and considerations for patient safety, the quality of medical images is usually limited, leading to sub-optimal fusion performance, and affecting the depth of image analysis by the physician. Thus, there is an urgent need for a technology that can both enhance image resolution and integrate multi-modal information. Although current image processing methods can effectively address image fusion and super-resolution individually, solving both problems synchronously remains extremely challenging. In this paper, we propose TFS-Diff, a simultaneously realize tri-modal medical image fusion and super-resolution model. Specially, TFS-Diff is based on the diffusion model generation of a random iterative denoising process. We also develop a simple objective function and the proposed fusion super-resolution loss, effectively evaluates the uncertainty in the fusion and ensures the stability of the optimization process. And the channel attention module is proposed to effectively integrate key information from different modalities for clinical diagnosis, avoiding information loss caused by multiple image processing. Extensive experiments on public Harvard datasets show that TFS-Diff significantly surpass the existing state-of-the-art methods in both quantitative and visual evaluations. The source code will be available at GitHub.

TSJNet: A Multi-modality Target and Semantic Awareness Joint-driven Image Fusion Network

Multi-modality image fusion involves integrating complementary information from different modalities into a single image. Current methods primarily focus on enhancing image fusion with a single advanced task such as incorporating semantic or object-related information into the fusion process. This method creates challenges in achieving multiple objectives simultaneously. We introduce a target and semantic awareness joint-driven fusion network called TSJNet. TSJNet comprises fusion, detection, and segmentation subnetworks arranged in a series structure. It leverages object and semantically relevant information derived from dual high-level tasks to guide the fusion network. Additionally, We propose a local significant feature extraction module with a double parallel branch structure to fully capture the fine-grained features of cross-modal images and foster interaction among modalities, targets, and segmentation information. We conducted extensive experiments on four publicly available datasets (MSRS, M3FD, RoadScene, and LLVIP). The results demonstrate that TSJNet can generate visually pleasing fused results, achieving an average increase of 2.84% and 7.47% in object detection and segmentation mAP @0.5 and mIoU, respectively, compared to the state-of-the-art methods.