98,040 research outputs found
A Novel Metric Approach Evaluation For The Spatial Enhancement Of Pan-Sharpened Images
Various and different methods can be used to produce high-resolution
multispectral images from high-resolution panchromatic image (PAN) and
low-resolution multispectral images (MS), mostly on the pixel level. The
Quality of image fusion is an essential determinant of the value of processing
images fusion for many applications. Spatial and spectral qualities are the two
important indexes that used to evaluate the quality of any fused image.
However, the jury is still out of fused image's benefits if it compared with
its original images. In addition, there is a lack of measures for assessing the
objective quality of the spatial resolution for the fusion methods. So, an
objective quality of the spatial resolution assessment for fusion images is
required. Therefore, this paper describes a new approach proposed to estimate
the spatial resolution improve by High Past Division Index (HPDI) upon
calculating the spatial-frequency of the edge regions of the image and it deals
with a comparison of various analytical techniques for evaluating the Spatial
quality, and estimating the colour distortion added by image fusion including:
MG, SG, FCC, SD, En, SNR, CC and NRMSE. In addition, this paper devotes to
concentrate on the comparison of various image fusion techniques based on pixel
and feature fusion technique.Comment: arXiv admin note: substantial text overlap with arXiv:1110.497
A Fusion Framework for Camouflaged Moving Foreground Detection in the Wavelet Domain
Detecting camouflaged moving foreground objects has been known to be
difficult due to the similarity between the foreground objects and the
background. Conventional methods cannot distinguish the foreground from
background due to the small differences between them and thus suffer from
under-detection of the camouflaged foreground objects. In this paper, we
present a fusion framework to address this problem in the wavelet domain. We
first show that the small differences in the image domain can be highlighted in
certain wavelet bands. Then the likelihood of each wavelet coefficient being
foreground is estimated by formulating foreground and background models for
each wavelet band. The proposed framework effectively aggregates the
likelihoods from different wavelet bands based on the characteristics of the
wavelet transform. Experimental results demonstrated that the proposed method
significantly outperformed existing methods in detecting camouflaged foreground
objects. Specifically, the average F-measure for the proposed algorithm was
0.87, compared to 0.71 to 0.8 for the other state-of-the-art methods.Comment: 13 pages, accepted by IEEE TI
Registration and Fusion of Multi-Spectral Images Using a Novel Edge Descriptor
In this paper we introduce a fully end-to-end approach for multi-spectral
image registration and fusion. Our method for fusion combines images from
different spectral channels into a single fused image by different approaches
for low and high frequency signals. A prerequisite of fusion is a stage of
geometric alignment between the spectral bands, commonly referred to as
registration. Unfortunately, common methods for image registration of a single
spectral channel do not yield reasonable results on images from different
modalities. For that end, we introduce a new algorithm for multi-spectral image
registration, based on a novel edge descriptor of feature points. Our method
achieves an accurate alignment of a level that allows us to further fuse the
images. As our experiments show, we produce a high quality of multi-spectral
image registration and fusion under many challenging scenarios
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