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Image De-Quantization Using Generative Models as Priors
[article]
2020
arXiv
pre-print
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In this effort we incorporate generative modeling of the ideal image as a suitable prior information. ...
Image quantization is used in several applications aiming in reducing the number of available colors in an image and therefore its size. ...
ACKNOWLEDGEMENT This work was supported by the US National Science Foundation under Grant CIF 1513373, through Rutgers University. ...
arXiv:2007.07923v2
fatcat:4bwolguspjde3jheg5empgusau
One-Bit Measurements With Adaptive Thresholds
2012
IEEE Signal Processing Letters
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Our approach allows the one-bit quantizer to use thresholds on the real line. ...
We introduce a new method for adaptive one-bit quantization of linear measurements and propose an algorithm for the recovery of signals based on generalized approximate message passing (GAMP). ...
The main contributions of this work are as follows: • An adaptation of the message-passing de-quantization algorithm of [14] to the problem of reconstruction from one-bit measurements. ...
doi:10.1109/lsp.2012.2209640
fatcat:iptzbvxmu5harehiqt6uaqte4u
Scientific Image Restoration Anywhere
[article]
2019
arXiv
pre-print
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Specifically, we evaluate deployments of TomoGAN, an image-denoising model based on generative adversarial networks developed for low-dose x-ray imaging, on the Google Edge TPU and NVIDIA Jetson. ...
Edge computing devices can be useful in this context, as their low cost and compact form factor permit them to be co-located with the experimental apparatus. ...
Department of Energy, Office of Science, Advanced Scientific Computing Research, under Contract DE-AC02-06CH11357. This research was accomplished when V. ...
arXiv:1911.05878v1
fatcat:fu4ckrnqwrguxdh4gmbmfxu3wu
Co-Saliency Detection Based on Hierarchical Segmentation
2014
IEEE Signal Processing Letters
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On the basis of fine segmentation, regional histograms are used to measure regional similarities between region pairs in the image set, and regional contrasts within each image are exploited to evaluate ...
Finally, the global similarity of each region is derived based on regional similarity measures, and then effectively integrated with intra-saliency map and object prior map to generate the co-saliency ...
From top to bottom: some original images in four image sets, binary ground truths, co-saliency maps generated using Fu's model [9] and our model, respectively. such as grassland and sky regions in the ...
doi:10.1109/lsp.2013.2292873
fatcat:oaazjftconh5bjxeqauqmuqfey
Reconstruction from Periodic Nonlinearities, With Applications to HDR Imaging
[article]
2017
arXiv
pre-print
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Our techniques can be potentially useful for reducing the measurement complexity of high dynamic range (HDR) imaging systems, with little loss in reconstruction quality. ...
We consider the problem of reconstructing signals and images from periodic nonlinearities. ...
Note that the use of sparse recovery here is generic, and we could in principle use any other prior model of relevance to the specific imaging application. ...
arXiv:1710.00109v1
fatcat:7cyx73hwlnckbbejndtcp6xkxy
Unified Multivariate Gaussian Mixture for Efficient Neural Image Compression
[article]
2022
arXiv
pre-print
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Modeling latent variables with priors and hyperpriors is an essential problem in variational image compression. ...
This encourages us to propose a novel vectorized prior. Specifically, a multivariate Gaussian mixture is proposed with means and covariances to be estimated. ...
We generalize prior as a unified multivariate Gaussian mixture.
Figure 3 . 3 Figure 3. ...
arXiv:2203.10897v1
fatcat:j22nsfsbuncdvmtiken23upmwm
An Infrared Array Sensor-Based Approach for Activity Detection, Combining Low-Cost Technology with Advanced Deep Learning Techniques
2022
Sensors
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We then classify the images/sequences of images depending on the activities the subject is performing using a hybrid deep learning model combining a Convolutional Neural Network (CNN) and a Long Short-Term ...
The process of data augmentation is performed by a Conditional Generative Adversarial Network (CGAN). ...
Generally speaking, the most commonly used methods for image restoration in computer vision are learned prior [27] and explicit prior [28] . ...
doi:10.3390/s22103898
pmid:35632305
pmcid:PMC9145665
fatcat:yyarpwhisndhjhpwh233igoqmi
End-to-end optimized image compression with competition of prior distributions
[article]
2021
arXiv
pre-print
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Note that this fulltext copy is not of the "primary" version of this work. The version it corresponds to is:
2021 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) unpublished doi:10.1109/cvprw53098.2021.00212 fatcat:6hydnwuv6nczvncxni2r7vyumm
During inference, this table is used by an entropy coder as a look-up-table to determine the best prior for each spatial location. ...
We instead propose a compression scheme that uses a single convolutional autoencoder and multiple learned prior distributions working as a competition of experts. ...
In our experiments, CDF tables generation in the encoding step takes 0.17 to 0.48 as much time with a 64-priors model as it does with the HP model (depending on the precision of the HP model). ...
arXiv:2111.09172v1
fatcat:zfvph3bumvf5rhj5jvl72se45i
Learned Video Compression via Joint Spatial-Temporal Correlation Exploration
2020
PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE
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We suggest an one-stage learning approach to encapsulate flow as quantized features from consecutive frames which is then entropy coded with adaptive contexts conditioned on joint spatial-temporal priors ...
Joint priors are embedded in autoregressive spatial neighbors, co-located hyper elements and temporal neighbors using ConvLSTM recurrently. ...
Most existing image compressions apply Generalized Divisive Normalization (GDN) as non-linear transform to de-correlate spatialchannel redundancy (Minnen, Ballé, and Toderici 2018; . ...
doi:10.1609/aaai.v34i07.6825
fatcat:naduixdarnfy3ebtcw55ht2h5e
Learned Video Compression via Joint Spatial-Temporal Correlation Exploration
[article]
2019
arXiv
pre-print
Preserved Fulltext
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We suggest an one-stage learning approach to encapsulate flow as quantized features from consecutive frames which is then entropy coded with adaptive contexts conditioned on joint spatial-temporal priors ...
Joint priors are embedded in autoregressive spatial neighbors, co-located hyper elements and temporal neighbors using ConvLSTM recurrently. ...
Most existing image compressions apply Generalized Divisive Normalization (GDN) as non-linear transform to de-correlate spatialchannel redundancy (Minnen, Ballé, and Toderici 2018; . ...
arXiv:1912.06348v1
fatcat:h6chbcl52nbwtbpx6hrrzj7fme
Forensic Analysis of Linear and Nonlinear Image Filtering Using Quantization Noise
2016
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
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In the proposed method, JPEG quantization noise is obtained using natural image prior and quantization noise models. ...
Real world forgeries are generally followed by the application of enhancement techniques such as filtering and/or conversion of the image format to suppress the forgery artifacts. ...
PROPOSED SCHEME Given an image, the quantization noise model and the image prior models described in section 2 are used to extract quantization noise. ...
doi:10.1145/2857069
fatcat:p7v2akwlhjhi5efpmepvvqteci
Level Lines as Global Minimizers of Energy Functionals in Image Segmentation
[chapter]
2000
Lecture Notes in Computer Science
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We propose a variational framework for determining global minimizers of rough energy functionals used in image segmentation. ...
Segmentation is achieved by minimizing an energy model, which is comprised of two parts: the rst part is the interaction between the observed data and the model, the second is a regularity term. ...
The last prior model can be re-de ned to nd large regions with low/high intensity in the image (see Figs. 2{3). Similar results were obtained using an entropy prior. ...
doi:10.1007/3-540-45053-x_16
fatcat:if22gp3yprbphj76qmrlt67ebe
Modeling Realistic Degradations in Non-blind Deconvolution
[article]
2018
arXiv
pre-print
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Most image deblurring methods assume an over-simplistic image formation model and as a result are sensitive to more realistic image degradations. ...
We propose a novel variational framework, that explicitly handles pixel saturation, noise, quantization, as well as non-linear camera response function due to e.g., gamma correction. ...
Let us denote (10) as the convexified quantization energy. ...
arXiv:1806.01097v1
fatcat:aj24htcwxbfldkqwqhrghc22re
Deep Attentive Generative Adversarial Network for Photo-Realistic Image De-Quantization
[article]
2020
arXiv
pre-print
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to recover as much useful information as possible. ...
Until now, this is the first attempt to apply Generative Adversarial Network (GAN) framework for image de-quantization. ...
We use the same concept for the generating of HBD images. However, most GAN-based models [21] , [22] for image generation are built using convolutional layers. ...
arXiv:2004.03150v1
fatcat:plmkqkgjcja7patyq6mi66mxqi
High-resolution images from low-resolution compressed video
2003
IEEE Signal Processing Magazine
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Acknowledgments The work of Segall and Katsaggelos was supported in part by the Motorola Center for Communications, Northwestern University, while the work of Molina was supported by the "Comision Nacional de ...
Having considered the relationship between LR and HR images prior to compression, let us turn our attention to the compression process. ...
This is equivalent to using the noninformative prior for both the original HR image and displacement data so that P P k ( ) ( ) f d ∝ ∝ const and const. (17) In these approaches, the noise model determines ...
doi:10.1109/msp.2003.1203208
fatcat:vm2pdxrobff6jfblvngmsbxnsa
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