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Image De-Quantization Using Generative Models as Priors [article]

Kalliopi Basioti, George V. Moustakides
2020 arXiv   pre-print
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

U. S. Kamilov, A. Bourquard, A. Amini, M. Unser
2012 IEEE Signal Processing Letters  
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]

Vibhatha Abeykoon, Zhengchun Liu, Rajkumar Kettimuthu, Geoffrey Fox, Ian Foster
2019 arXiv   pre-print
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

Zhi Liu, Wenbin Zou, Lina Li, Liquan Shen, Olivier Le Meur
2014 IEEE Signal Processing Letters  
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]

Viraj Shah, Mohammadreza Soltani, Chinmay Hegde
2017 arXiv   pre-print
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]

Xiaosu Zhu, Jingkuan Song, Lianli Gao, Feng Zheng, Heng Tao Shen
2022 arXiv   pre-print
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

Muthukumar Krishnan Arumugasamy, Mondher Bouazizi, Tomoaki Ohtsuki
2022 Sensors  
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]

Benoit Brummer, Christophe De Vleeschouwer
2021 arXiv   pre-print
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

Haojie Liu, Han Shen, Lichao Huang, Ming Lu, Tong Chen, Zhan Ma
2020 PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE  
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]

Haojie Liu, Han shen, Lichao Huang, Ming Lu, Tong Chen, Zhan Ma
2019 arXiv   pre-print
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

Hareesh Ravi, A. V. Subramanyam, Sabu Emmanuel
2016 ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)  
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]

Charles Kervrann, Mark Hoebeke, Alain Trubuil
2000 Lecture Notes in Computer Science  
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]

Jérémy Anger, Mauricio Delbracio, Gabriele Facciolo
2018 arXiv   pre-print
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]

Yang Zhang, Changhui Hu, Xiaobo Lu
2020 arXiv   pre-print
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

C.A. Segall, R. Molina, A.K. Katsaggelos
2003 IEEE Signal Processing Magazine  
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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