Physical Perception Network and an All-weather Multi-modality Benchmark
for Adverse Weather Image Fusion
- URL: http://arxiv.org/abs/2402.02090v1
- Date: Sat, 3 Feb 2024 09:02:46 GMT
- Title: Physical Perception Network and an All-weather Multi-modality Benchmark
for Adverse Weather Image Fusion
- Authors: Xilai Li, Wuyang Liu, Xiaosong Li, Haishu Tan
- Abstract summary: Multi-modality image fusion (MMIF) integrates the complementary information from different modal images to provide comprehensive and objective interpretation of a scenes.
Existing MMIF methods lack the ability to resist different weather interferences in real-life scenarios.
We have established a benchmark for MMIF research under extreme weather conditions.
- Score: 4.3773535988950725
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Multi-modality image fusion (MMIF) integrates the complementary information
from different modal images to provide comprehensive and objective
interpretation of a scenes. However, existing MMIF methods lack the ability to
resist different weather interferences in real-life scenarios, preventing them
from being useful in practical applications such as autonomous driving. To
bridge this research gap, we proposed an all-weather MMIF model. Regarding deep
learning architectures, their network designs are often viewed as a black box,
which limits their multitasking capabilities. For deweathering module, we
propose a physically-aware clear feature prediction module based on an
atmospheric scattering model that can deduce variations in light transmittance
from both scene illumination and depth. For fusion module, We utilize a
learnable low-rank representation model to decompose images into low-rank and
sparse components. This highly interpretable feature separation allows us to
better observe and understand images. Furthermore, we have established a
benchmark for MMIF research under extreme weather conditions. It encompasses
multiple scenes under three types of weather: rain, haze, and snow, with each
weather condition further subdivided into various impact levels. Extensive
fusion experiments under adverse weather demonstrate that the proposed
algorithm has excellent detail recovery and multi-modality feature extraction
capabilities.
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