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Ill-posed problems in early vision

M. Bertero, T.A. Poggio, V. Torre
1988 Proceedings of the IEEE  
Specific topics in early vision and their regularization are then analyzed rigorously, characterizing existence, uniqueness, a n d stability o f solutions.  ...  We review here the relevant mathematical results o n ill-posed a n d ill-conditioned problems a n d introduce the formal aspects of regularization theory in the linear a n d nonlinear case.  ...  Section VI contains some results related to inverse nonlinear prob- In the second part we show that several approaches, recently proposed by many authors to solve problems of early vision, using smoothness  ... 
doi:10.1109/5.5962 fatcat:zrblexiofnb5njxgd6xksamd2y

Insect-based visual motion detection with contrast adaptation

Patrick A. Shoemaker, David C. O'Carroll, Bjorn F. Andresen, Gabor F. Fulop
2005 Infrared Technology and Applications XXXI  
Our approach accounts for the basic nonlinear and temporal bandpass characteristics of early vision, includes a simple model of motion adaptation as a form of contrast gain control, followed by a correlational  ...  input signal amplitude due to the nonlinearity imposed by the logarithmic transformation in early vision.  ... 
doi:10.1117/12.609470 fatcat:wiyq3os3lrhflf2w6evvmj2c74

A One-Dimensional Analog VLSI Implementation for Nonlinear Real-Time Signal Preprocessing

K. Wiehler, J. Heers, C. Schnörr, H.S. Stiehl, R.-R. Grigat
2001 Real-time imaging  
R econstruction of given noisy data is an ill-posed problem and a computationally intensive task. Nonlinear regularization techniques are used to find a unique solution under certain constraints.  ...  Results from applying the 1D chip to nonlinear smoothing of twodimensional image data will also be given correspondence.  ...  Build upon Mead's fundamental research, Harris [3, 19] implemented nonlinear resistive networks for early vision tasks.  ... 
doi:10.1006/rtim.1999.0218 fatcat:dmesnsnrsfc2jhr7vgakd47cki

Recursive Deep Residual Learning for Single Image Dehazing

Yixin Du, Xin Li
2018 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)  
Such novel reformulation enables us to directly estimate a nonlinear mapping from input hazy images to output dehazed ones (i.e., bypassing the unnecessary step of transmission map estimation).  ...  The dehazing-denoising analogy also motivates us to leverage the strategy of iterative regularization from denoising to dehazing -i.e., we propose to recursively feed the dehazed image back to the input  ...  In previous approaches, Estimating A and t(x) are necessary steps for image dehazing; in this work, we propose to bypass them and take a direct approach of learning a nonlinear mapping from I(x) to J(x  ... 
doi:10.1109/cvprw.2018.00116 dblp:conf/cvpr/DuL18 fatcat:qhrdli2a7rbyzp6dxx4aup5iuu

Deep Learning for Image/Video Restoration and Super-resolution

A. Murat Tekalp
2022 Foundations and Trends in Computer Graphics and Vision  
We can consider learned image/video restoration and SR as learning either a nonlinear regressive mapping from degraded to ideal images based on the universal approximation theorem, or a generative model  ...  An important benefit of data-driven deep learning approaches to image processing is that neural models can be optimized for any differentiable loss function, including perceptual loss functions, leading  ...  Early iterative regularization methods include nonlinear Landweber iterations, iterative back-projection, or projection onto convex sets (POCS) methods.  ... 
doi:10.1561/0600000100 fatcat:5keqxf3lingubhlgrptdpq42xy

Methods for Postprocessing in Single-Step Diffuse Optical Tomography [chapter]

Alexander B., Vitaly V., Dmitry V., Olga V., Vladimir V.
2008 Computer Vision  
Acknowledgments The authors would like to thank V. N. Ananijchuk, S. V. Kolchugin, V. M. Kryukov and G. N. Rykovanov for their considerable long-standing encouragement and support. References  ...  Preconditioned iterative regularization by truncating the iterations is an effective approach to accelerate the rate of convergence (Nagy et al., 2004) .  ...  In this case a special approach to image extrapolation is needed (Konovalov et al., 2006c) .  ... 
doi:10.5772/6162 fatcat:gh22nobqefbo3baoa7zq2secty

Regularization operators for natural images based on nonlinear perception models

J. Gutierrez, F.J. Ferri, J. Malo
2006 IEEE Transactions on Image Processing  
Biological visual systems have evolved to capture these relations.We propose the use of this biological behavior to build regularization operators as an alternative to simple statistical models.  ...  Image restoration requires some a priori knowledge of the solution. Some of the conventional regularization techniques are based on the estimation of the power spectrum density.  ...  They would also like to thank Dr. G. Camps for useful discussions on AR models, Dr. J. Viru for his insight on natural images features, Prof. J. M. Artigas, Dr. A. B. Watson, Dr. A. Ahumada, and Dr.  ... 
doi:10.1109/tip.2005.860345 pmid:16435549 fatcat:4vjlgxyifbajtlageb34vz2kx4

Efficient visual system processing of spatial and luminance statistics in representational and non-representational art

Daniel J. Graham, Jay D. Friedenberg, Daniel N. Rockmore, Bernice E. Rogowitz, Thrasyvoulos N. Pappas
2009 Human Vision and Electronic Imaging XIV  
We show empirically that elements of contemporary approaches to high-dynamic range tone-mapping-which are themselves deeply rooted in an understanding of early visual system coding-are present in the way  ...  An efficient visual system could make a quick and reasonable guess as to the relationship of a given image to others (i.e., its context) by extracting these basic statistics early in the visual stream,  ...  Van Gogh-and other painters-would appear to employ a representational approach that is similar to a modern HDR tone mapping strategy.  ... 
doi:10.1117/12.817185 dblp:conf/hvei/GrahamFR09 fatcat:y27z3ie2xze5tid6qp4zqebbdm

Neural system identification for large populations separating "what" and "where" [article]

David A. Klindt, Alexander S. Ecker, Thomas Euler, Matthias Bethge
2018 arXiv   pre-print
Here, we show that a major bottleneck for fitting convolutional neural networks (CNNs) to neural data is the estimation of the individual receptive field locations, a problem that has been scratched only  ...  Our network scales well to thousands of neurons and short recordings and can be trained end-to-end.  ...  Government is authorized to reproduce and distribute reprints for Governmental purposes notwithstanding any copyright annotation thereon.  ... 
arXiv:1711.02653v2 fatcat:ck5jtzg4knas7dyqitedaik64u

Scripta Qumranica Electronica (2016–2021)

Bronson Brown-deVost
2016 Hebrew Bible and Ancient Israel  
Nonlinear Static (Pushover) Analysis (POA) is a time saving and simple assessment procedure prosposed in Eurocode 8 (EC8). However, its reliability in designing structure still remains a question.  ...  A B S T R A C T Soft storey building is popular due to the functional and aesthetic purpose, despite its weakness in resisting seismic excitation.  ...  The modern approach to earthquake resistant design is an attempt to design/retrofit buildings with predictable seismic performance through detreminsitic/probabilistic approach.  ... 
doi:10.1628/186870316x14805961757430 fatcat:lpvksxfwafdoleo4k2opugbmui

Physically based adaptive preconditioning for early vision

Shang-Hong Lai, B.C. Vemuri
1997 IEEE Transactions on Pattern Analysis and Machine Intelligence  
Several problems in early vision have been formulated in the past in a regularization framework. These problems, when discretized, lead to large sparse linear systems.  ...  The adaptation of the preconditioner to an early vision problem is achieved via the explicit use of the spectral characteristics of the regularization filter in conjunction with the data.  ...  Application to Early Vision Problems The general method for constructing our adaptive preconditioner for the early vision problems formulated in a regularization framework was discussed in Section 4.  ... 
doi:10.1109/34.601247 fatcat:cvoxdrzel5b53jp4cgdwcqfp6e

A new energy minimization framework and sparse linear system for path planning and shape from shading

Adrian M. Peter, Karthik S. Gurumoorthy, Mark Moyou, Anand Rangarajan
2014 Proceedings of the 2014 Indian Conference on Computer Vision Graphics and Image Processing - ICVGIP '14  
The limiting behavior, for a particular case, of this linear differential equation can be shown to converge to the nonlinear eikonal.  ...  advantages of a linear systems approach.  ...  However, lost in this flurry of advancing nonlinear solvers was a completely alternative approach, one which allows you to rigorously approximate solutions to the nonlinear eikonal by instead solving a  ... 
doi:10.1145/2683483.2683498 dblp:conf/icvgip/PeterGMR14 fatcat:c47dmqufxva4plafi5ljko4o5u

Radon Transform based Modified Nonlinear Access for Segmentation of Mammogram Application

2019 International journal of recent technology and engineering  
The proposed approach of nonlinear method of segmentation for which specific images of mammogram are consideredusing probability weighted force stopping function and Bayesian rules to extract the weak  ...  In this paper a novel and application oriented mammogram segmentation using Nonlinear level set method and Radon Transform proposed.  ...  CONCLUSION AND DISCUSSION This article construct novel approach of nonlinear with stopping force function based on probability weightedtoresolves problems which counter in early section.  ... 
doi:10.35940/ijrte.b1094.078219 fatcat:jrokk4fh3nd5hotdorjcd5uypi

Neural Encoding for Human Visual Cortex with Deep Neural Networks Learning "What" and "Where" [article]

Haibao Wang, Lijie Huang, Changde Du, Dan Li, Bo Wang, Huiguang He
2019 bioRxiv   pre-print
AbstractNeural encoding, a crucial aspect to understand human brain information processing system, aims to establish a quantitative relationship between the stimuli and the evoked brain activities.  ...  The two forms of regularizations: sparsity and smoothness, are also adopted in our modeling approach, so that the receptive field can be estimated automatically without prior assumptions about shapes.  ...  This modeling approach (Fig 1) of neural 7 encoding links fMRI signals at the millimeter scale to neural response at the micron scale, 8 providing a non-invasive approach to revealing the nonlinear  ... 
doi:10.1101/861989 fatcat:ow4llnj4mray5ig2lmjcs5zwnu

Statistical regularities of art images and natural scenes: Spectra, sparseness and nonlinearities

Daniel Graham, David Field
2007 Spatial Vision  
Because artists must produce images that can be seen by a visual system that is thought to take advantage of statistical regularities in natural scenes, artists are likely to replicate many of these regularities  ...  But when a compressive nonlinearity was applied to the images, both the paintings' sparseness and the modeled responses to the paintings showed the same or greater sparseness compared to the natural scenes  ...  We wish to acknowledge the many current and former staff of the H. F.  ... 
doi:10.1163/156856807782753877 pmid:18073056 fatcat:63k4zwwcq5hp7cfefbxgwrok2e
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