49 research outputs found
Multi-Sensor Image Fusion Based on Moment Calculation
An image fusion method based on salient features is proposed in this paper.
In this work, we have concentrated on salient features of the image for fusion
in order to preserve all relevant information contained in the input images and
tried to enhance the contrast in fused image and also suppressed noise to a
maximum extent. In our system, first we have applied a mask on two input images
in order to conserve the high frequency information along with some low
frequency information and stifle noise to a maximum extent. Thereafter, for
identification of salience features from sources images, a local moment is
computed in the neighborhood of a coefficient. Finally, a decision map is
generated based on local moment in order to get the fused image. To verify our
proposed algorithm, we have tested it on 120 sensor image pairs collected from
Manchester University UK database. The experimental results show that the
proposed method can provide superior fused image in terms of several
quantitative fusion evaluation index.Comment: 5 pages, International Conferenc
BigFUSE: Global Context-Aware Image Fusion in Dual-View Light-Sheet Fluorescence Microscopy with Image Formation Prior
Light-sheet fluorescence microscopy (LSFM), a planar illumination technique
that enables high-resolution imaging of samples, experiences defocused image
quality caused by light scattering when photons propagate through thick
tissues. To circumvent this issue, dualview imaging is helpful. It allows
various sections of the specimen to be scanned ideally by viewing the sample
from opposing orientations. Recent image fusion approaches can then be applied
to determine in-focus pixels by comparing image qualities of two views locally
and thus yield spatially inconsistent focus measures due to their limited
field-of-view. Here, we propose BigFUSE, a global context-aware image fuser
that stabilizes image fusion in LSFM by considering the global impact of photon
propagation in the specimen while determining focus-defocus based on local
image qualities. Inspired by the image formation prior in dual-view LSFM, image
fusion is considered as estimating a focus-defocus boundary using Bayes
Theorem, where (i) the effect of light scattering onto focus measures is
included within Likelihood; and (ii) the spatial consistency regarding
focus-defocus is imposed in Prior. The expectation-maximum algorithm is then
adopted to estimate the focus-defocus boundary. Competitive experimental
results show that BigFUSE is the first dual-view LSFM fuser that is able to
exclude structured artifacts when fusing information, highlighting its
abilities of automatic image fusion.Comment: paper in MICCAI 202
Real-MFF: A Large Realistic Multi-focus Image Dataset with Ground Truth
Multi-focus image fusion, a technique to generate an all-in-focus image from
two or more partially-focused source images, can benefit many computer vision
tasks. However, currently there is no large and realistic dataset to perform
convincing evaluation and comparison of algorithms in multi-focus image fusion.
Moreover, it is difficult to train a deep neural network for multi-focus image
fusion without a suitable dataset. In this letter, we introduce a large and
realistic multi-focus dataset called Real-MFF, which contains 710 pairs of
source images with corresponding ground truth images. The dataset is generated
by light field images, and both the source images and the ground truth images
are realistic. To serve as both a well-established benchmark for existing
multi-focus image fusion algorithms and an appropriate training dataset for
future development of deep-learning-based methods, the dataset contains a
variety of scenes, including buildings, plants, humans, shopping malls, squares
and so on. We also evaluate 10 typical multi-focus algorithms on this dataset
for the purpose of illustration
Image Fusion with Contrast Improving and Feature Preserving
The goal of image fusion is to obtain a fused image that contains most significant information in all input images which were captured by different sensors from the same scene. In particular, the fusion process should improve the contrast and keep the integrity of significant features from input images. In this paper, we propose a region-based image fusion method to fuse spatially registered visible and infrared images while improving the contrast and preserving the significant features of input images. At first, the proposed method decomposes input images into base layers and detail layers using a bilateral filter. Then the base layers of the input images are segmented into regions. Third, a region-based decision map is proposed to represent the importance of every region. The decision map is obtained by calculating the weights of regions according to the gray-level difference between each region and its neighboring regions in the base layers. At last, the detail layers and the base layers are separately fused by different fusion rules based on the same decision map to generate a final fused image. Experimental results qualitatively and quantitatively demonstrate that the proposed method can improve the contrast of fused images and preserve more features of input images than several previous image fusion methods