534 research outputs found
Atmospheric turbulence mitigation for sequences with moving objects using recursive image fusion
This paper describes a new method for mitigating the effects of atmospheric
distortion on observed sequences that include large moving objects. In order to
provide accurate detail from objects behind the distorting layer, we solve the
space-variant distortion problem using recursive image fusion based on the Dual
Tree Complex Wavelet Transform (DT-CWT). The moving objects are detected and
tracked using the improved Gaussian mixture models (GMM) and Kalman filtering.
New fusion rules are introduced which work on the magnitudes and angles of the
DT-CWT coefficients independently to achieve a sharp image and to reduce
atmospheric distortion, respectively. The subjective results show that the
proposed method achieves better video quality than other existing methods with
competitive speed.Comment: IEEE International Conference on Image Processing 201
Object recognition in atmospheric turbulence scenes
The influence of atmospheric turbulence on acquired surveillance imagery
poses significant challenges in image interpretation and scene analysis.
Conventional approaches for target classification and tracking are less
effective under such conditions. While deep-learning-based object detection
methods have shown great success in normal conditions, they cannot be directly
applied to atmospheric turbulence sequences. In this paper, we propose a novel
framework that learns distorted features to detect and classify object types in
turbulent environments. Specifically, we utilise deformable convolutions to
handle spatial turbulent displacement. Features are extracted using a feature
pyramid network, and Faster R-CNN is employed as the object detector.
Experimental results on a synthetic VOC dataset demonstrate that the proposed
framework outperforms the benchmark with a mean Average Precision (mAP) score
exceeding 30%. Additionally, subjective results on real data show significant
improvement in performance
Learning to Interpret Fluid Type Phenomena via Images
Learning to interpret fluid-type phenomena via images is a long-standing challenging problem in computer vision. The problem becomes even more challenging when the fluid medium is highly dynamic and refractive due to its transparent nature. Here, we consider imaging through such refractive fluid media like water and air. For water, we design novel supervised learning-based algorithms to recover its 3D surface as well as the highly distorted underground patterns. For air, we design a state-of-the-art unsupervised learning algorithm to predict the distortion-free image given a short sequence of turbulent images. Specifically, we design a deep neural network that estimates the depth and normal maps of a fluid surface by analyzing the refractive distortion of a reference background pattern. Regarding the recovery of severely downgraded underwater images due to the refractive distortions caused by water surface fluctuations, we present the distortion-guided network (DG-Net) for restoring distortion-free underwater images. The key idea is to use a distortion map to guide network training. The distortion map models the pixel displacement caused by water refraction. Furthermore, we present a novel unsupervised network to recover the latent distortion-free image. The key idea is to model non-rigid distortions as deformable grids. Our network consists of a grid deformer that estimates the distortion field and an image generator that outputs the distortion-free image. By leveraging the positional encoding operator, we can simplify the network structure while maintaining fine spatial details in the recovered images. We also develop a combinational deep neural network that can simultaneously perform recovery of the latent distortion-free image as well as 3D reconstruction of the transparent and dynamic fluid surface. Through extensive experiments on simulated and real captured fluid images, we demonstrate that our proposed deep neural networks outperform the current state-of-the-art on solving specific tasks
Artificial Intelligence in the Creative Industries: A Review
This paper reviews the current state of the art in Artificial Intelligence
(AI) technologies and applications in the context of the creative industries. A
brief background of AI, and specifically Machine Learning (ML) algorithms, is
provided including Convolutional Neural Network (CNNs), Generative Adversarial
Networks (GANs), Recurrent Neural Networks (RNNs) and Deep Reinforcement
Learning (DRL). We categorise creative applications into five groups related to
how AI technologies are used: i) content creation, ii) information analysis,
iii) content enhancement and post production workflows, iv) information
extraction and enhancement, and v) data compression. We critically examine the
successes and limitations of this rapidly advancing technology in each of these
areas. We further differentiate between the use of AI as a creative tool and
its potential as a creator in its own right. We foresee that, in the near
future, machine learning-based AI will be adopted widely as a tool or
collaborative assistant for creativity. In contrast, we observe that the
successes of machine learning in domains with fewer constraints, where AI is
the `creator', remain modest. The potential of AI (or its developers) to win
awards for its original creations in competition with human creatives is also
limited, based on contemporary technologies. We therefore conclude that, in the
context of creative industries, maximum benefit from AI will be derived where
its focus is human centric -- where it is designed to augment, rather than
replace, human creativity
Simulation of anisoplanatic turbulence for images and videos
Turbulence is a common phenomenon in the atmosphere and can generate a variety of distortions in an image. This can cause further image processing tasks to struggle due to lack of detail in the resulting turbulence affected imagery. It is therefore useful to attempt to remove such distortions as a post processing step. However, the development of such algorithms is difficult due to the complex nature of turbulence data acquisition. To alleviate these issues, this paper presents the development of a turbulence simulator that is capable of imparting the effects of a turbulent atmosphere onto clean images and videos. This work also provides a large, publicly available dataset that can be used as a benchmark. The simulator and dataset will be valuable resources in the field of turbulence mitigation. Indeed, the simulator allows researchers to simulate specific turbulent conditions for any application as required; while the dataset provides the ability to make use of turbulent data without the expensive time commitment of simulation
Computational Image Formation
At the pinnacle of computational imaging is the co-optimization of camera and
algorithm. This, however, is not the only form of computational imaging. In
problems such as imaging through adverse weather, the bigger challenge is how
to accurately simulate the forward degradation process so that we can
synthesize data to train reconstruction models and/or integrating the forward
model as part of the reconstruction algorithm. This article introduces the
concept of computational image formation (CIF). Compared to the standard
inverse problems where the goal is to recover the latent image
from the observation , CIF shifts the
focus to designing an approximate mapping such that
while giving a better image
reconstruction result. The word ``computational'' highlights the fact that the
image formation is now replaced by a numerical simulator. While matching nature
remains an important goal, CIF pays even greater attention on strategically
choosing an so that the reconstruction performance is
maximized.
The goal of this article is to conceptualize the idea of CIF by elaborating
on its meaning and implications. The first part of the article is a discussion
on the four attributes of a CIF simulator: accurate enough to mimic
, fast enough to be integrated as part of the reconstruction,
providing a well-posed inverse problem when plugged into the reconstruction,
and differentiable in the backpropagation sense. The second part of the article
is a detailed case study based on imaging through atmospheric turbulence. The
third part of the article is a collection of other examples that fall into the
category of CIF. Finally, thoughts about the future direction and
recommendations to the community are shared
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