4,135 research outputs found
One-Stage Cascade Refinement Networks for Infrared Small Target Detection
Single-frame InfraRed Small Target (SIRST) detection has been a challenging
task due to a lack of inherent characteristics, imprecise bounding box
regression, a scarcity of real-world datasets, and sensitive localization
evaluation. In this paper, we propose a comprehensive solution to these
challenges. First, we find that the existing anchor-free label assignment
method is prone to mislabeling small targets as background, leading to their
omission by detectors. To overcome this issue, we propose an all-scale
pseudo-box-based label assignment scheme that relaxes the constraints on scale
and decouples the spatial assignment from the size of the ground-truth target.
Second, motivated by the structured prior of feature pyramids, we introduce the
one-stage cascade refinement network (OSCAR), which uses the high-level head as
soft proposals for the low-level refinement head. This allows OSCAR to process
the same target in a cascade coarse-to-fine manner. Finally, we present a new
research benchmark for infrared small target detection, consisting of the
SIRST-V2 dataset of real-world, high-resolution single-frame targets, the
normalized contrast evaluation metric, and the DeepInfrared toolkit for
detection. We conduct extensive ablation studies to evaluate the components of
OSCAR and compare its performance to state-of-the-art model-driven and
data-driven methods on the SIRST-V2 benchmark. Our results demonstrate that a
top-down cascade refinement framework can improve the accuracy of infrared
small target detection without sacrificing efficiency. The DeepInfrared
toolkit, dataset, and trained models are available at
https://github.com/YimianDai/open-deepinfrared to advance further research in
this field.Comment: Submitted to TGR
Infrared and Visible Image Fusion using a Deep Learning Framework
In recent years, deep learning has become a very active research tool which
is used in many image processing fields. In this paper, we propose an effective
image fusion method using a deep learning framework to generate a single image
which contains all the features from infrared and visible images. First, the
source images are decomposed into base parts and detail content. Then the base
parts are fused by weighted-averaging. For the detail content, we use a deep
learning network to extract multi-layer features. Using these features, we use
l_1-norm and weighted-average strategy to generate several candidates of the
fused detail content. Once we get these candidates, the max selection strategy
is used to get final fused detail content. Finally, the fused image will be
reconstructed by combining the fused base part and detail content. The
experimental results demonstrate that our proposed method achieves
state-of-the-art performance in both objective assessment and visual quality.
The Code of our fusion method is available at
https://github.com/hli1221/imagefusion_deeplearningComment: 6 pages, 6 figures, 2 tables, ICPR 2018(accepted
Randomized Dynamic Mode Decomposition
This paper presents a randomized algorithm for computing the near-optimal
low-rank dynamic mode decomposition (DMD). Randomized algorithms are emerging
techniques to compute low-rank matrix approximations at a fraction of the cost
of deterministic algorithms, easing the computational challenges arising in the
area of `big data'. The idea is to derive a small matrix from the
high-dimensional data, which is then used to efficiently compute the dynamic
modes and eigenvalues. The algorithm is presented in a modular probabilistic
framework, and the approximation quality can be controlled via oversampling and
power iterations. The effectiveness of the resulting randomized DMD algorithm
is demonstrated on several benchmark examples of increasing complexity,
providing an accurate and efficient approach to extract spatiotemporal coherent
structures from big data in a framework that scales with the intrinsic rank of
the data, rather than the ambient measurement dimension. For this work we
assume that the dynamics of the problem under consideration is evolving on a
low-dimensional subspace that is well characterized by a fast decaying singular
value spectrum
MDLatLRR: A novel decomposition method for infrared and visible image fusion
Image decomposition is crucial for many image processing tasks, as it allows
to extract salient features from source images. A good image decomposition
method could lead to a better performance, especially in image fusion tasks. We
propose a multi-level image decomposition method based on latent low-rank
representation(LatLRR), which is called MDLatLRR. This decomposition method is
applicable to many image processing fields. In this paper, we focus on the
image fusion task. We develop a novel image fusion framework based on MDLatLRR,
which is used to decompose source images into detail parts(salient features)
and base parts. A nuclear-norm based fusion strategy is used to fuse the detail
parts, and the base parts are fused by an averaging strategy. Compared with
other state-of-the-art fusion methods, the proposed algorithm exhibits better
fusion performance in both subjective and objective evaluation.Comment: IEEE Trans. Image Processing 2020, 14 pages, 17 figures, 3 table
Fast filtering image fusion
© 2017 SPIE and IS & T. Image fusion aims at exploiting complementary information in multimodal images to create a single composite image with extended information content. An image fusion framework is proposed for different types of multimodal images with fast filtering in the spatial domain. First, image gradient magnitude is used to detect contrast and image sharpness. Second, a fast morphological closing operation is performed on image gradient magnitude to bridge gaps and fill holes. Third, the weight map is obtained from the multimodal image gradient magnitude and is filtered by a fast structure-preserving filter. Finally, the fused image is composed by using a weighed-sum rule. Experimental results on several groups of images show that the proposed fast fusion method has a better performance than the state-of-the-art methods, running up to four times faster than the fastest baseline algorithm
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