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Cluster-based probability model applied to image restoration and compression
Proceedings of ICASSP '94. IEEE International Conference on Acoustics, Speech and Signal Processing
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Properties of this model are reviewed, and its power demonstrated by application to image restoration and compression. ...
A recently introduced model circumvents some of these di culties while maintaining accuracy su cient to account for much of the high-order, nonlinear statistical interdependence of samples. ...
SUMMARY We reviewed some properties of a recently proposed cluster-based probability model, and discussed the model's application to image restoration and compression. ...
doi:10.1109/icassp.1994.389408
dblp:conf/icassp/PopatP94
fatcat:haz52gylsjecpmn4llut4ux5a4
Cluster-based probability model and its application to image and texture processing
1997
IEEE Transactions on Image Processing
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Experimental results are presented in the following applications: image restoration, image and texture compression, and texture classi cation. ...
We develop, analyze, and apply a speci c form of mixture modeling for density estimation, within the context of image and texture processing. ...
Acknowledgments Thanks to the anonymous reviewers for their helpful suggestions, and to Michael I. Jordan for bringing to our attention the relationship between the k-means and EM algorithms. ...
doi:10.1109/83.551697
pmid:18282922
fatcat:cgdrvnosoncgxhn6ggpvdezhje
An Assessment of Density Effects on MRI Brain Images Using Lossy and Lossless Coding
2018
Zenodo
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The Project proposes the region based Image compression technique based on the clustering model and hybrid compression technique. ...
The regions are encoded by lossless and lossy technique to increase the compression ratio and preserve the image quality. ...
We applied our ROI based hybrid compression method to three datasets of 20 slices each.8 by 8 and 16 by 16 block sizes will be used. ...
doi:10.5281/zenodo.1410998
fatcat:2524636ayvb37eg324z3ceyshm
Forward-adaptive method for context-based compression of large binary images
1999
Software, Practice & Experience
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The proposed method is a two-stage combination of forward-adaptive modeling and backward-adaptive context based compression with re-initialization of statistics. ...
A method for compressing large binary images is proposed for applications where spatial access to the image is required. ...
The only differences are that the QM-coder is reinitialized and the model is restored each time the compression of a new cluster starts. The states are restored using the RestoreState function. ...
doi:10.1002/(sici)1097-024x(199909)29:11<943::aid-spe266>3.0.co;2-k
fatcat:jvqn3vovszepzpuonsjyxd56am
Maximum-likelihood parameter estimation for image ringing-artifact removal
2001
IEEE transactions on circuits and systems for video technology (Print)
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The proposed algorithm and its simplified approximation are applied to JPEG2000 compressed images. Our results show effective and efficient removal of ringing artifacts. . ...
His research interests are in image sequence processing, objectbased image recovery, color processing, and the restoration of images degraded by compression and acquisition distortions. ...
His recent research interests focused on image and video processing and humancomputer interface. Dr. ...
doi:10.1109/76.937440
fatcat:qbqfdpn5enh65ekf4ftgt7h3ji
Image Compression Transmission Algorithm Based on the Singular Value Decomposition Applied in the Wireless Multimedia Sensor Networks
2015
International Journal of Signal Processing, Image Processing and Pattern Recognition
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image information, and then send image block to ordinary nodes in the cluster; the ordinary nodes in the clusters will adaptive compress the block image and send the data to cluster head node; and then ...
, the cluster head node will send the compressed data to the base station. ...
This paper adopts the cluster type layer of network topology model to realize the image compression. ...
doi:10.14257/ijsip.2015.8.3.17
fatcat:6nu3vz6m7rhhtesca2woxmgctu
Model-Based Iterative Restoration for Binary Document Image Compression with Dictionary Learning
2017
2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
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., scanned) binary document image degrades the image quality and harms the compression ratio through breaking the pattern repentance and adding entropy to the document images. ...
After the restoration, we use this dictionary (from the same cost function) to encode the restored image following the symbol-dictionary framework by JBIG2 standard with the lossless mode. ...
Conclusion We propose a model-based iterative restoration with dictionary learning method to solve a joint optimization regards of image quality and compression ratio. ...
doi:10.1109/cvpr.2017.72
dblp:conf/cvpr/GuoLAB17
fatcat:kmobkxlwhzcwljvy4wxsvuxg6a
Model-based Iterative Restoration for Binary Document Image Compression with Dictionary Learning
[article]
2017
arXiv
pre-print
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., scanned) binary document image degrades the image quality and harms the compression ratio through breaking the pattern repentance and adding entropy to the document images. ...
After the restoration, we use this dictionary (from the same cost function) to encode the restored image following the symbol-dictionary framework by JBIG2 standard with the lossless mode. ...
Conclusion We propose a model-based iterative restoration with dictionary learning method to solve a joint optimization regards of image quality and compression ratio. ...
arXiv:1704.07019v1
fatcat:yx6vqschuvguvoo46tab32v7ii
Enhanced JBIG-based compression for satisfying objectives of engineering document management system
1998
Optical Engineering: The Journal of SPIE
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Existing image compression algorithms [e.g., Group 4 (G4) and Joint Bilevel Image Experts Group (JBIG)] offer efficient solutions to the storage problem but do not sufficiently support other objectives ...
The compression performance of the proposed method is only 10% worse than that of JBIG, and at the same time, spatial access to a compressed file is achieved. ...
Acknowledgments The work of Pasi Fränti was supported by a grant from the Academy of Finland, and the work of Eugene I. Ageenko by a grant from the Centre for International Mobility. ...
doi:10.1117/1.601668
fatcat:piqsaeqfwnf4pfrlawopyyt2xi
An Image Compression Scheme in Wireless Multimedia Sensor Networks Based on NMF
2017
Information
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Compressed images are transmitted to the station by the cluster head node and received from ordinary nodes. The station takes on the image restoration. ...
With the goal of addressing the issue of image compression in wireless multimedia sensor networks with high recovered quality and low energy consumption, an image compression and transmission scheme based ...
Based on this, we selected the layer-cluster topology. This model can decompose images more efficiently. ...
doi:10.3390/info8010026
fatcat:h6jafiagm5h5ljhakragwi6ksi
A REVIEW ON DIGITAL IMAGE PROCESSING: APPLICATIONS, TECHNIQUES AND APPROACHES IN VARIOUS FIELDS
2020
Zenodo
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It also gives thorough insight of various techniques involved in the image processing such as image aquisition, image segmentation, image transformation, image restoration, image compression etc. ...
The advantages and limitations of certain strategies and algorithms used in image processing such as SIFT, SURF, BRIEF and ORB are discussed in detail. ...
Restoration of the images might be achieved via two types of model viz. degradation Model and restoration model [17] . ...
doi:10.5281/zenodo.3953440
fatcat:nhpr3lzwv5g2vjibks7u3tftxm
A Study on Image Segmentation Method for Image Processing
[chapter]
2021
Advances in Parallel Computing
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Image processing makes to simplify the image representation in order to analyze the images. So many algorithms are developed for segmenting images, based on the certain feature of the pixel. ...
This comparison study is useful for increasing accuracy and performance of segmentation methods in various image processing domains. ...
It resolves the image repairing problems occurred when the image restoration process. Criminisi algorithm and watershed image algorithm is applied to the large amount of image set. ...
doi:10.3233/apc210223
fatcat:3xqxd53dn5cg5dmh3qwv225di4
Lossless compression of large binary images in digital spatial libraries
2000
Computers & graphics
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The proposed technique achieves higher compression rates and allows dense tiling of images down to 50;50 pixels without sacri"cing the compression performance. ...
A method for lossless compression of large binary images is proposed for applications where spatial access to the image is needed. ...
Eugene Ageenko acknowledges the Moscow State University, Dept. of Applied Mathematics, where during the graduate and post-graduate studies he started this research. ...
doi:10.1016/s0097-8493(99)00140-5
fatcat:qexlrsep7vfafccyvfbikq6mcq
A multiscale regularized restoration algorithm for XMM-Newton data
[article]
2001
arXiv
pre-print
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We introduce a new multiscale restoration algorithm for images with few photons counts and its use for denoising XMM data. ...
Contrary to other algorithms the signal restoration process is the same whatever the signal to noise ratio is. ...
Pratt and N. Aghanim for allowing us to use the XMM images of A2163 and for useful discussions on the restored images. ...
arXiv:astro-ph/0106138v1
fatcat:53dpsvls7neyjprewfksqwidri
Image Super-resolution via Feature-augmented Random Forest
[article]
2017
arXiv
pre-print
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This original-compressed coupled feature sets scheme unifies the unsupervised LSH evaluation on both image super-resolution and content-based image retrieval (CBIR). ...
Furthermore, a fine-tuned FARF model can compare to or (in many cases) outperform some recent stateof-the-art deep-learning-based algorithms. ...
Based on this observation, we obtain the coarse estimation of an HR image ̂ by applying IBP to the corresponding input LR image . ...
arXiv:1712.05248v1
fatcat:bep2ejds3jfaneuyubh4xgb7ym
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