54,999 research outputs found
Image enhancement from a stabilised video sequence
The aim of video stabilisation is to create a new video sequence where the motions (i.e. rotations, translations) and scale differences between frames (or parts of a frame) have effectively been removed. These stabilisation effects can be obtained via digital video processing techniques which use the information extracted from the video sequence itself, with no need for additional hardware or knowledge about camera physical motion.
A video sequence usually contains a large overlap between successive frames, and regions of the same scene are sampled at different positions. In this paper, this multiple sampling is combined to achieve images with a higher spatial resolution. Higher resolution imagery play an important role in assisting in the identification of people, vehicles, structures or objects of interest captured by surveillance cameras or by video cameras used in face recognition, traffic monitoring, traffic law reinforcement, driver assistance and automatic vehicle guidance systems
X-ray image separation via coupled dictionary learning
In support of art investigation, we propose a new source sepa- ration method
that unmixes a single X-ray scan acquired from double-sided paintings. Unlike
prior source separation meth- ods, which are based on statistical or structural
incoherence of the sources, we use visual images taken from the front- and
back-side of the panel to drive the separation process. The coupling of the two
imaging modalities is achieved via a new multi-scale dictionary learning
method. Experimental results demonstrate that our method succeeds in the
discrimination of the sources, while state-of-the-art methods fail to do so.Comment: To be presented at the IEEE International Conference on Image
Processing (ICIP), 201
Multi-modal dictionary learning for image separation with application in art investigation
In support of art investigation, we propose a new source separation method
that unmixes a single X-ray scan acquired from double-sided paintings. In this
problem, the X-ray signals to be separated have similar morphological
characteristics, which brings previous source separation methods to their
limits. Our solution is to use photographs taken from the front and back-side
of the panel to drive the separation process. The crux of our approach relies
on the coupling of the two imaging modalities (photographs and X-rays) using a
novel coupled dictionary learning framework able to capture both common and
disparate features across the modalities using parsimonious representations;
the common component models features shared by the multi-modal images, whereas
the innovation component captures modality-specific information. As such, our
model enables the formulation of appropriately regularized convex optimization
procedures that lead to the accurate separation of the X-rays. Our dictionary
learning framework can be tailored both to a single- and a multi-scale
framework, with the latter leading to a significant performance improvement.
Moreover, to improve further on the visual quality of the separated images, we
propose to train coupled dictionaries that ignore certain parts of the painting
corresponding to craquelure. Experimentation on synthetic and real data - taken
from digital acquisition of the Ghent Altarpiece (1432) - confirms the
superiority of our method against the state-of-the-art morphological component
analysis technique that uses either fixed or trained dictionaries to perform
image separation.Comment: submitted to IEEE Transactions on Images Processin
- …