Image fusion meets deep learning: A survey and perspective
Image fusion, which refers to extracting and then combining the most meaningful information
from different source images, aims to generate a single image that is more informative and …
from different source images, aims to generate a single image that is more informative and …
A review on multimodal medical image fusion: Compendious analysis of medical modalities, multimodal databases, fusion techniques and quality metrics
Background and objectives Over the past two decades, medical imaging has been
extensively apply to diagnose diseases. Medical experts continue to have difficulties for …
extensively apply to diagnose diseases. Medical experts continue to have difficulties for …
Cddfuse: Correlation-driven dual-branch feature decomposition for multi-modality image fusion
Multi-modality (MM) image fusion aims to render fused images that maintain the merits of
different modalities, eg, functional highlight and detailed textures. To tackle the challenge in …
different modalities, eg, functional highlight and detailed textures. To tackle the challenge in …
SwinFusion: Cross-domain long-range learning for general image fusion via swin transformer
This study proposes a novel general image fusion framework based on cross-domain long-
range learning and Swin Transformer, termed as SwinFusion. On the one hand, an attention …
range learning and Swin Transformer, termed as SwinFusion. On the one hand, an attention …
SuperFusion: A versatile image registration and fusion network with semantic awareness
Image fusion aims to integrate complementary information in source images to synthesize a
fused image comprehensively characterizing the imaging scene. However, existing image …
fused image comprehensively characterizing the imaging scene. However, existing image …
PIAFusion: A progressive infrared and visible image fusion network based on illumination aware
Infrared and visible image fusion aims to synthesize a single fused image containing salient
targets and abundant texture details even under extreme illumination conditions. However …
targets and abundant texture details even under extreme illumination conditions. However …
Image fusion in the loop of high-level vision tasks: A semantic-aware real-time infrared and visible image fusion network
Infrared and visible image fusion aims to synthesize a single fused image that not only
contains salient targets and abundant texture details but also facilitates high-level vision …
contains salient targets and abundant texture details but also facilitates high-level vision …
DDFM: denoising diffusion model for multi-modality image fusion
Multi-modality image fusion aims to combine different modalities to produce fused images
that retain the complementary features of each modality, such as functional highlights and …
that retain the complementary features of each modality, such as functional highlights and …
LLVIP: A visible-infrared paired dataset for low-light vision
It is very challenging for various visual tasks such as image fusion, pedestrian detection, and
image-to-image translation in low light conditions due to the loss of effective target areas. In …
image-to-image translation in low light conditions due to the loss of effective target areas. In …
RFN-Nest: An end-to-end residual fusion network for infrared and visible images
In the image fusion field, the design of deep learning-based fusion methods is far from
routine. It is invariably fusion-task specific and requires a careful consideration. The most …
routine. It is invariably fusion-task specific and requires a careful consideration. The most …