21,490 research outputs found
An Efficient Algorithm for Video Super-Resolution Based On a Sequential Model
In this work, we propose a novel procedure for video super-resolution, that
is the recovery of a sequence of high-resolution images from its low-resolution
counterpart. Our approach is based on a "sequential" model (i.e., each
high-resolution frame is supposed to be a displaced version of the preceding
one) and considers the use of sparsity-enforcing priors. Both the recovery of
the high-resolution images and the motion fields relating them is tackled. This
leads to a large-dimensional, non-convex and non-smooth problem. We propose an
algorithmic framework to address the latter. Our approach relies on fast
gradient evaluation methods and modern optimization techniques for
non-differentiable/non-convex problems. Unlike some other previous works, we
show that there exists a provably-convergent method with a complexity linear in
the problem dimensions. We assess the proposed optimization method on {several
video benchmarks and emphasize its good performance with respect to the state
of the art.}Comment: 37 pages, SIAM Journal on Imaging Sciences, 201
Accurate and robust image superresolution by neural processing of local image representations
Image superresolution involves the processing of an image sequence to generate a still image with higher resolution. Classical approaches, such as bayesian MAP methods, require iterative minimization procedures, with high computational costs. Recently, the authors proposed a method to tackle this problem, based on the use of a hybrid MLP-PNN architecture. In this paper, we present a novel superresolution method, based on an evolution of this concept, to incorporate the use of local image models. A neural processing stage receives as input the value of model coefficients on local windows. The data dimension-ality is firstly reduced by application of PCA. An MLP, trained on synthetic se-quences with various amounts of noise, estimates the high-resolution image data. The effect of varying the dimension of the network input space is exam-ined, showing a complex, structured behavior. Quantitative results are presented showing the accuracy and robustness of the proposed method
Real Time Turbulent Video Perfecting by Image Stabilization and Super-Resolution
Image and video quality in Long Range Observation Systems (LOROS) suffer from
atmospheric turbulence that causes small neighbourhoods in image frames to
chaotically move in different directions and substantially hampers visual
analysis of such image and video sequences. The paper presents a real-time
algorithm for perfecting turbulence degraded videos by means of stabilization
and resolution enhancement. The latter is achieved by exploiting the turbulent
motion. The algorithm involves generation of a reference frame and estimation,
for each incoming video frame, of a local image displacement map with respect
to the reference frame; segmentation of the displacement map into two classes:
stationary and moving objects and resolution enhancement of stationary objects,
while preserving real motion. Experiments with synthetic and real-life
sequences have shown that the enhanced videos, generated in real time, exhibit
substantially better resolution and complete stabilization for stationary
objects while retaining real motion.Comment: Submitted to The Seventh IASTED International Conference on
Visualization, Imaging, and Image Processing (VIIP 2007) August, 2007 Palma
de Mallorca, Spai
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