939 research outputs found
Analysis of deep learning architectures for turbulence mitigation in long-range imagery
In long range imagery, the atmosphere along the line of sight can result in unwanted visual effects. Random variations in the refractive index of the air causes light to shift and distort. When captured by a camera, this randomly induced variation results in blurred and spatially distorted images. The removal of such effects is greatly desired. Many traditional methods are able to reduce the effects of turbulence within images, however they require complex optimisation procedures or have large computational complexity. The use of deep learning for image processing has now become commonplace, with neural networks being able to outperform traditional methods in many fields. This paper presents an evaluation of various deep learning architectures on the task of turbulence mitigation. The core disadvantage of deep learning is the dependence on a large quantity of relevant data. For the task of turbulence mitigation, real life data is difficult to obtain, as a clean undistorted image is not always obtainable. Turbulent images were therefore generated with the use of a turbulence simulator. This was able to accurately represent atmospheric conditions and apply the resulting spatial distortions onto clean images. This paper provides a comparison between current state of the art image reconstruction convolutional neural networks. Each network is trained on simulated turbulence data. They are then assessed on a series of test images. It is shown that the networks are unable to provide high quality output images. However, they are shown to be able to reduce the effects of spatial warping within the test images. This paper provides critical analysis into the effectiveness of the application of deep learning. It is shown that deep learning has potential in this field, and can be used to make further improvements in the future
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
Single Frame Atmospheric Turbulence Mitigation: A Benchmark Study and A New Physics-Inspired Transformer Model
Image restoration algorithms for atmospheric turbulence are known to be much
more challenging to design than traditional ones such as blur or noise because
the distortion caused by the turbulence is an entanglement of spatially varying
blur, geometric distortion, and sensor noise. Existing CNN-based restoration
methods built upon convolutional kernels with static weights are insufficient
to handle the spatially dynamical atmospheric turbulence effect. To address
this problem, in this paper, we propose a physics-inspired transformer model
for imaging through atmospheric turbulence. The proposed network utilizes the
power of transformer blocks to jointly extract a dynamical turbulence
distortion map and restore a turbulence-free image. In addition, recognizing
the lack of a comprehensive dataset, we collect and present two new real-world
turbulence datasets that allow for evaluation with both classical objective
metrics (e.g., PSNR and SSIM) and a new task-driven metric using text
recognition accuracy. Both real testing sets and all related code will be made
publicly available.Comment: This paper is accepted as a poster at ECCV 202
Deep learning for anisoplanatic optical turbulence mitigation in long-range imaging
We present a deep learning approach for restoring images degraded by atmospheric optical turbulence. We consider the case of terrestrial imaging over long ranges with a wide field-of-view. This produces an anisoplanatic imaging scenario where turbulence warping and blurring vary spatially across the image. The proposed turbulence mitigation (TM) method assumes that a sequence of short-exposure images is acquired. A block matching (BM) registration algorithm is applied to the observed frames for dewarping, and the resulting images are averaged. A convolutional neural network (CNN) is then employed to perform spatially adaptive restoration. We refer to the proposed TM algorithm as the block matching and CNN (BM-CNN) method. Training the CNN is accomplished using simulated data from a fast turbulence simulation tool capable of producing a large amount of degraded imagery from declared truth images rapidly. Testing is done using independent data simulated with a different well-validated numerical wave-propagation simulator. Our proposed BM-CNN TM method is evaluated in a number of experiments using quantitative metrics. The quantitative analysis is made possible by virtue of having truth imagery from the simulations. A number of restored images are provided for subjective evaluation. We demonstrate that the BM-CNN TM method outperforms the benchmark methods in the scenarios tested
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
Image Restoration for Remote Sensing: Overview and Toolbox
Remote sensing provides valuable information about objects or areas from a
distance in either active (e.g., RADAR and LiDAR) or passive (e.g.,
multispectral and hyperspectral) modes. The quality of data acquired by
remotely sensed imaging sensors (both active and passive) is often degraded by
a variety of noise types and artifacts. Image restoration, which is a vibrant
field of research in the remote sensing community, is the task of recovering
the true unknown image from the degraded observed image. Each imaging sensor
induces unique noise types and artifacts into the observed image. This fact has
led to the expansion of restoration techniques in different paths according to
each sensor type. This review paper brings together the advances of image
restoration techniques with particular focuses on synthetic aperture radar and
hyperspectral images as the most active sub-fields of image restoration in the
remote sensing community. We, therefore, provide a comprehensive,
discipline-specific starting point for researchers at different levels (i.e.,
students, researchers, and senior researchers) willing to investigate the
vibrant topic of data restoration by supplying sufficient detail and
references. Additionally, this review paper accompanies a toolbox to provide a
platform to encourage interested students and researchers in the field to
further explore the restoration techniques and fast-forward the community. The
toolboxes are provided in https://github.com/ImageRestorationToolbox.Comment: This paper is under review in GRS
Convolutional Deblurring for Natural Imaging
In this paper, we propose a novel design of image deblurring in the form of
one-shot convolution filtering that can directly convolve with naturally
blurred images for restoration. The problem of optical blurring is a common
disadvantage to many imaging applications that suffer from optical
imperfections. Despite numerous deconvolution methods that blindly estimate
blurring in either inclusive or exclusive forms, they are practically
challenging due to high computational cost and low image reconstruction
quality. Both conditions of high accuracy and high speed are prerequisites for
high-throughput imaging platforms in digital archiving. In such platforms,
deblurring is required after image acquisition before being stored, previewed,
or processed for high-level interpretation. Therefore, on-the-fly correction of
such images is important to avoid possible time delays, mitigate computational
expenses, and increase image perception quality. We bridge this gap by
synthesizing a deconvolution kernel as a linear combination of Finite Impulse
Response (FIR) even-derivative filters that can be directly convolved with
blurry input images to boost the frequency fall-off of the Point Spread
Function (PSF) associated with the optical blur. We employ a Gaussian low-pass
filter to decouple the image denoising problem for image edge deblurring.
Furthermore, we propose a blind approach to estimate the PSF statistics for two
Gaussian and Laplacian models that are common in many imaging pipelines.
Thorough experiments are designed to test and validate the efficiency of the
proposed method using 2054 naturally blurred images across six imaging
applications and seven state-of-the-art deconvolution methods.Comment: 15 pages, for publication in IEEE Transaction Image Processin
Physics-Driven Turbulence Image Restoration with Stochastic Refinement
Image distortion by atmospheric turbulence is a stochastic degradation, which
is a critical problem in long-range optical imaging systems. A number of
research has been conducted during the past decades, including model-based and
emerging deep-learning solutions with the help of synthetic data. Although fast
and physics-grounded simulation tools have been introduced to help the
deep-learning models adapt to real-world turbulence conditions recently, the
training of such models only relies on the synthetic data and ground truth
pairs. This paper proposes the Physics-integrated Restoration Network (PiRN) to
bring the physics-based simulator directly into the training process to help
the network to disentangle the stochasticity from the degradation and the
underlying image. Furthermore, to overcome the ``average effect" introduced by
deterministic models and the domain gap between the synthetic and real-world
degradation, we further introduce PiRN with Stochastic Refinement (PiRN-SR) to
boost its perceptual quality. Overall, our PiRN and PiRN-SR improve the
generalization to real-world unknown turbulence conditions and provide a
state-of-the-art restoration in both pixel-wise accuracy and perceptual
quality. Our codes are available at \url{https://github.com/VITA-Group/PiRN}.Comment: Accepted by ICCV 202
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