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RAISR: Rapid and Accurate Image Super Resolution [article]

Yaniv Romano, John Isidoro, Peyman Milanfar
2016 arXiv   pre-print
Given an image, we wish to produce an image of larger size with significantly more pixels and higher image quality. This is generally known as the Single Image Super-Resolution (SISR) problem.  ...  The idea is that with sufficient training data (corresponding pairs of low and high resolution images) we can learn set of filters (i.e. a mapping) that when applied to given image that is not in the training  ...  CONCLUSION In this paper we proposed a rapid and accurate learning-based approach for single image super-resolution (called RAISR).  ... 
arXiv:1606.01299v3 fatcat:tqihdzxkl5fybmwet4hdpmwtju

Improved image denoising via RAISR with fewer filters

Theingi Zin, Yusuke Nakahara, Takuro Yamaguchi, Masaaki Ikehara
2021 Computational Visual Media  
To tackle this problem, an improvement to the pioneering RAISR approach (rapid and accurate image super-resolution), is applied to rapidly post-process the denoised image.  ...  It gives performance comparable to state-of-the-art super-resolution techniques at low computational cost, preserving important image structures well.  ...  a Keio Leading-edge Laboratory of Science and Technology (KLL) Ph.D.  ... 
doi:10.1007/s41095-021-0213-0 fatcat:rpg74hutbndsjgnn43p63oqvey

Top Downloads in IEEE Xplore [Reader's Choice]

H. Vicky Zhao
2020 IEEE Signal Processing Magazine  
This article proposes a rapid and accurate learning-based approach for image superresolution, known as rapid and accurate image super resolution (RAISR).  ...  and Accurate Image Super Resolution Loss Functions for Image Restoration With Neural Networks Kamilov, U.S.; Papadopoulos, I.N.; Shoreh, M.H.; Goy, A.; Vonesch, C.; Unser, C.M.; Psaltis, D.  ... 
doi:10.1109/msp.2020.3002527 fatcat:k47kne24rjfujkptio4beicupu

Defect Detection Method for Electric Multiple Units Key Components Based on Deep Learning

Bing Zhao, Mingrui Dai, Ping Li, Rui Xue, Xiaoning Ma
2020 IEEE Access  
MPDD includes two stages, component detection stage improves RPN anchor mechanism and way of feature fusion to promote detection performance, defect classification stage combines super-resolution strategy  ...  Thus as an effective inspection approach for defects, image detection becomes significantly important for operation and maintenance in the railway industry.  ...  The comprehensive super-resolution strategy is composed of Rapid and Accurate Image Super Resolution (RAISR) [33] , SRGAN [34] , and biquadratic interpolations.  ... 
doi:10.1109/access.2020.3009654 fatcat:6iqlzgnuvrblznrj72fyg3t4em

Fast and Accurate Image Super Resolution by Deep CNN with Skip Connection and Network in Network [article]

Jin Yamanaka, Shigesumi Kuwashima, Takio Kurita
2020 arXiv   pre-print
We propose a highly efficient and faster Single Image Super-Resolution (SISR) model with Deep Convolutional neural networks (Deep CNN).  ...  Code is available at https://github.com/jiny2001/dcscn-super-resolution  ...  Therefore, small sets of Deep-Learning models could be made and combined to work as an ensemble model to fix real and complex problems.  ... 
arXiv:1707.05425v7 fatcat:jh4gtvyspveahanh5vsaapjkxm

Single image super-resolution using back-propagation neural networks

Mohammad S. Hasan, Salman Taseen Haque
2017 2017 20th International Conference of Computer and Information Technology (ICCIT)  
The results of Bicubic with ANN are also compared with state of the art super-resolution techniques like SRCNN. Bicubic with ANN produces 1.48% higher SSIM and 3.44% higher PSNR than SRCNN.  ...  There are several existing mathematical algorithms for colour image upscaling like Nearest Neighbour, Bicubic and Bilinear.  ...  For example, Google has recently developed an algorithm called "Rapid and Accurate Image Super Resolution (RAISR)" [14] .  ... 
doi:10.1109/iccitechn.2017.8281778 fatcat:6xz42mu3sjhyxib2scrxyt3cvm

BLADE: Filter Learning for General Purpose Computational Photography [article]

Pascal Getreuer, Ignacio Garcia-Dorado, John Isidoro, Sungjoon Choi, Frank Ong, Peyman Milanfar
2017 arXiv   pre-print
The Rapid and Accurate Image Super Resolution (RAISR) method of Romano, Isidoro, and Milanfar is a computationally efficient image upscaling method using a trained set of filters.  ...  We describe a generalization of RAISR, which we name Best Linear Adaptive Enhancement (BLADE).  ...  Filter selection is not trained; it is performed by hand-crafted features derived from the image structure tensor, which are effective for adapting behavior to the local image geometry, but is probably  ... 
arXiv:1711.10700v2 fatcat:3futakwu6nfrfkbqjj2izj7xuu

CARN: Convolutional Anchored Regression Network for Fast and Accurate Single Image Super-Resolution [chapter]

Yawei Li, Eirikur Agustsson, Shuhang Gu, Radu Timofte, Luc Van Gool
2019 Lecture Notes in Computer Science  
Thus, we propose the convolutional anchored regression network (CARN) for fast and accurate single image super-resolution (SISR).  ...  Although the accuracy of super-resolution (SR) methods based on convolutional neural networks (CNN) soars high, the complexity and computation also explode with the increased depth and width of the network  ...  Introduction Super-resolution (SR) refers to the recovery of high-resolution (HR) images containing high-frequency detail information from low-resolution (LR) images [10, 9, 27] .  ... 
doi:10.1007/978-3-030-11021-5_11 fatcat:stprpfetafgfppmmrixn6zgijy

Mobile Computational Photography: A Tour [article]

Mauricio Delbracio, Damien Kelly, Michael S. Brown, Peyman Milanfar
2021 arXiv   pre-print
In this paper, we give a brief history of mobile computational photography and describe some of the key technological components, including burst photography, noise reduction, and super-resolution.  ...  This transformation was enabled by advances in computational photography -the science and engineering of making great images from small form factor, mobile cameras.  ...  Acknowledgments The authors wish to acknowledge the computational imaging community of scholars and colleaguesindustrial and academic alike-whose work has led to the advances reported in this review.  ... 
arXiv:2102.09000v2 fatcat:m6vwjmwwwbf37m2syw5kfovoxe

A weighted least squares optimisation strategy for medical image super resolution via multiscale convolutional neural networks for healthcare applications

Bhawna Goyal, Dawa Chyophel Lepcha, Ayush Dogra, Shui-Hua Wang
2021 Complex & Intelligent Systems  
Recently, medical image super-resolution (SR) has emerged as an indispensable research subject in the community of image processing to address such limitations.  ...  SR is a classical computer vision operation that attempts to restore a visually sharp high-resolution images from the degraded low-resolution images.  ...  Yaniv Romano et al., on the other hand, suggested rapid and accurate image super resolution (RAISR) [31] that focused on shallow and quick learning.  ... 
doi:10.1007/s40747-021-00465-z fatcat:c2t3qd4yjvhhfgggi5d6pulfx4

Fast, Trainable, Multiscale Denoising [article]

Sungjoon Choi, John Isidoro, Pascal Getreuer, Peyman Milanfar
2018 arXiv   pre-print
Denoising is a fundamental imaging problem. Versatile but fast filtering has been demanded for mobile camera systems.  ...  This approach is trainable so that learned filters are capable of treating diverse noise patterns and artifacts.  ...  Rapid and accurate image super resolution (RAISR) [19] is an efficient edge-adaptive image upscaling method that uses structure tensor features to select a filter at each pixel from among a set of trained  ... 
arXiv:1802.06130v1 fatcat:rrfwxyxutzez3ojpzb3sumnxlm

Solving Image PDEs with a Shallow Network [article]

Pascal Tom Getreuer, Peyman Milanfar, Xiyang Luo
2021 arXiv   pre-print
However, when it comes to their use in imaging, conventional numerical methods for solving PDEs tend to require very fine grid resolution for stability, and as a result have impractically high computational  ...  This work applies BLADE (Best Linear Adaptive Enhancement), a shallow learnable filtering framework, to PDE solving, and shows that the resulting approach is efficient and accurate, operating more reliably  ...  It is the generalization of the Rapid and Accurate Image Super-Resolution (RAISR) method [21] to tasks other than image upscaling.  ... 
arXiv:2110.08327v1 fatcat:5tsg7l534reivficx3dp3twape

2021 IEEE Signal Processing Society Awards [Society News]

2022 IEEE Signal Processing Magazine  
, vol. 27, no. 8, August 2019. ■ Yaniv Romano, John Isidoro, and Peyman Milanfar, "RAISR: Rapid and Accurate Image Super Resolution," IEEE Transactions on Computational Imaging, vol. 3, no. 1, January  ...  Varshney, "for outstanding contributions in the fields of distributed inference and data fusion," and Charles Bouman, "for fundamental contributions to X-ray computed tomography and computational imaging  ... 
doi:10.1109/msp.2022.3152966 fatcat:so5ctjk3grc27frutaa7ajky2q

Wasserstein Patch Prior for Image Superresolution [article]

Johannes Hertrich, Antoine Houdard, Claudia Redenbach
2021 arXiv   pre-print
In this paper, we introduce a Wasserstein patch prior for superresolution of two- and three-dimensional images.  ...  Here, we assume that we have given (additionally to the low resolution observation) a reference image which has a similar patch distribution as the ground truth of the reconstruction.  ...  Isidoro, and P. Milanfar. RAISR: Rapid and accurate image super resolution. IEEE Transactions on Computational Imaging, 3(1):110–125, 2017. [32] L. I. Rudin, S. Osher, and E. Fatemi.  ... 
arXiv:2109.12880v2 fatcat:3ccrd36pqzabppkveahukkb3te

CNN-Based Real-Time Parameter Tuning for Optimizing Denoising Filter Performance [chapter]

Subhayan Mukherjee, Navaneeth Kamballur Kottayil, Xinyao Sun, Irene Cheng
2019 Lecture Notes in Computer Science  
We propose and train a simple, shallow CNN to predict in real time, the optimum filter parameter value, given the input noisy image.  ...  Each training example consists of a noisy input image (training data) and the filter parameter value that produces the best output (training label).  ...  An alternative approach that is used to solve image denoising problems was pioneered by Trainable nonlinear reaction diffusion (TNRD) [7] and Rapid and accurate image super resolution (RAISR) [25] ,  ... 
doi:10.1007/978-3-030-27202-9_10 fatcat:wsbfo4au6fdcjc3creuykbpkxu
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