46 research outputs found
DDRF: Denoising Diffusion Model for Remote Sensing Image Fusion
Denosing diffusion model, as a generative model, has received a lot of
attention in the field of image generation recently, thanks to its powerful
generation capability. However, diffusion models have not yet received
sufficient research in the field of image fusion. In this article, we introduce
diffusion model to the image fusion field, treating the image fusion task as
image-to-image translation and designing two different conditional injection
modulation modules (i.e., style transfer modulation and wavelet modulation) to
inject coarse-grained style information and fine-grained high-frequency and
low-frequency information into the diffusion UNet, thereby generating fused
images. In addition, we also discussed the residual learning and the selection
of training objectives of the diffusion model in the image fusion task.
Extensive experimental results based on quantitative and qualitative
assessments compared with benchmarks demonstrates state-of-the-art results and
good generalization performance in image fusion tasks. Finally, it is hoped
that our method can inspire other works and gain insight into this field to
better apply the diffusion model to image fusion tasks. Code shall be released
for better reproducibility
Source-Aware Spatial-Spectral-Integrated Double U-Net for Image Fusion
In image fusion tasks, pictures from different sources possess distinctive
properties, therefore treating them equally will lead to inadequate feature
extracting. Besides, multi-scaled networks capture information more
sufficiently than single-scaled models in pixel-wised problems. In light of
these factors, we propose a source-aware spatial-spectral-integrated double
U-shaped network called Net. The network is mainly composed of a
spatial U-net and a spectral U-net, which learn spatial details and spectral
characteristics discriminately and hierarchically. In contrast with most
previous works that simply apply concatenation to integrate spatial and
spectral information, a novel structure named the spatial-spectral block
(called Block) is specially designed to merge feature maps from
different sources effectively. Experiment results show that our method
outperforms the representative state-of-the-art (SOTA) approaches in both
quantitative and qualitative evaluations for a variety of image fusion
missions, including remote sensing pansharpening and hyperspectral image
super-resolution (HISR)
Pansharpening via Frequency-Aware Fusion Network with Explicit Similarity Constraints
The process of fusing a high spatial resolution (HR) panchromatic (PAN) image
and a low spatial resolution (LR) multispectral (MS) image to obtain an HRMS
image is known as pansharpening. With the development of convolutional neural
networks, the performance of pansharpening methods has been improved, however,
the blurry effects and the spectral distortion still exist in their fusion
results due to the insufficiency in details learning and the frequency mismatch
between MSand PAN. Therefore, the improvement of spatial details at the premise
of reducing spectral distortion is still a challenge. In this paper, we propose
a frequency-aware fusion network (FAFNet) together with a novel high-frequency
feature similarity loss to address above mentioned problems. FAFNet is mainly
composed of two kinds of blocks, where the frequency aware blocks aim to
extract features in the frequency domain with the help of discrete wavelet
transform (DWT) layers, and the frequency fusion blocks reconstruct and
transform the features from frequency domain to spatial domain with the
assistance of inverse DWT (IDWT) layers. Finally, the fusion results are
obtained through a convolutional block. In order to learn the correspondence,
we also propose a high-frequency feature similarity loss to constrain the HF
features derived from PAN and MS branches, so that HF features of PAN can
reasonably be used to supplement that of MS. Experimental results on three
datasets at both reduced- and full-resolution demonstrate the superiority of
the proposed method compared with several state-of-the-art pansharpening
models.Comment: 14 page