6,939 research outputs found
An FPGA implementation of pattern-Selective pyramidal image fusion
The aim of image fusion is to combine multiple images (from one or more sensors) into a single composite image that retains all useful data without introducing artefacts. Pattern-selective techniques attempt to identify and extract whole features in the source images to use in the composite. These techniques usually rely on multiresolution image representations such as Gaussian pyramids, which are localised in both the spatial and spatial-frequency domains, since they enable identification of features at many scales simultaneously. This paper presents an FPGA implementation of pyramidal decomposition and subsequent fusion of dual video streams. This is the first reported instance of a hardware implementation of pattern-selective pyramidal image fusion. Use of FPGA technology has enabled a design that can fuse dual video streams (greyscale VGA, 30fps) in real-time, and provides approximately 100 times speedup over a 2.8GHz Pentium-
DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs
In this work we address the task of semantic image segmentation with Deep
Learning and make three main contributions that are experimentally shown to
have substantial practical merit. First, we highlight convolution with
upsampled filters, or 'atrous convolution', as a powerful tool in dense
prediction tasks. Atrous convolution allows us to explicitly control the
resolution at which feature responses are computed within Deep Convolutional
Neural Networks. It also allows us to effectively enlarge the field of view of
filters to incorporate larger context without increasing the number of
parameters or the amount of computation. Second, we propose atrous spatial
pyramid pooling (ASPP) to robustly segment objects at multiple scales. ASPP
probes an incoming convolutional feature layer with filters at multiple
sampling rates and effective fields-of-views, thus capturing objects as well as
image context at multiple scales. Third, we improve the localization of object
boundaries by combining methods from DCNNs and probabilistic graphical models.
The commonly deployed combination of max-pooling and downsampling in DCNNs
achieves invariance but has a toll on localization accuracy. We overcome this
by combining the responses at the final DCNN layer with a fully connected
Conditional Random Field (CRF), which is shown both qualitatively and
quantitatively to improve localization performance. Our proposed "DeepLab"
system sets the new state-of-art at the PASCAL VOC-2012 semantic image
segmentation task, reaching 79.7% mIOU in the test set, and advances the
results on three other datasets: PASCAL-Context, PASCAL-Person-Part, and
Cityscapes. All of our code is made publicly available online.Comment: Accepted by TPAM
DeepFuse: A Deep Unsupervised Approach for Exposure Fusion with Extreme Exposure Image Pairs
We present a novel deep learning architecture for fusing static
multi-exposure images. Current multi-exposure fusion (MEF) approaches use
hand-crafted features to fuse input sequence. However, the weak hand-crafted
representations are not robust to varying input conditions. Moreover, they
perform poorly for extreme exposure image pairs. Thus, it is highly desirable
to have a method that is robust to varying input conditions and capable of
handling extreme exposure without artifacts. Deep representations have known to
be robust to input conditions and have shown phenomenal performance in a
supervised setting. However, the stumbling block in using deep learning for MEF
was the lack of sufficient training data and an oracle to provide the
ground-truth for supervision. To address the above issues, we have gathered a
large dataset of multi-exposure image stacks for training and to circumvent the
need for ground truth images, we propose an unsupervised deep learning
framework for MEF utilizing a no-reference quality metric as loss function. The
proposed approach uses a novel CNN architecture trained to learn the fusion
operation without reference ground truth image. The model fuses a set of common
low level features extracted from each image to generate artifact-free
perceptually pleasing results. We perform extensive quantitative and
qualitative evaluation and show that the proposed technique outperforms
existing state-of-the-art approaches for a variety of natural images.Comment: ICCV 201
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
Implementation and Validation of Visual and Infrared Image Fusion Techniques in C# .NET Environment
This paper presents the implementation of image fusion techniques by means of an image fusion application “C#ImFuse”, developed in C#.NET. C# programming language is a simple, type-safe, object-oriented language that allows programmers to build a variety of applications. C#ImFuse application implements four fusion methods viz., Alpha Blending (AB), Principle Component Analysis (PCA), Laplacian Pyramid (LP), and Discrete Wavelet Transform (DWT) for a visual and a thermal image (still images) and for real-time images of the Enhanced Vision System (EVS). The performance of these fusion techniques is evaluated using fusion performance metrics. LP based image fusion technique proved to provide better fusion when compared to the other techniques. Source code is provided so that the reader can understand the techniques and use for his research work
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