Titlebook: Handbook of Convex Optimization Methods in Imaging Science; Vishal Monga Book 2018 Springer International Publishing AG 2018 Optimization.

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书目名称Handbook of Convex Optimization Methods in Imaging Science影响因子(影响力)




书目名称Handbook of Convex Optimization Methods in Imaging Science影响因子(影响力)学科排名




书目名称Handbook of Convex Optimization Methods in Imaging Science网络公开度




书目名称Handbook of Convex Optimization Methods in Imaging Science网络公开度学科排名




书目名称Handbook of Convex Optimization Methods in Imaging Science被引频次




书目名称Handbook of Convex Optimization Methods in Imaging Science被引频次学科排名




书目名称Handbook of Convex Optimization Methods in Imaging Science年度引用




书目名称Handbook of Convex Optimization Methods in Imaging Science年度引用学科排名




书目名称Handbook of Convex Optimization Methods in Imaging Science读者反馈




书目名称Handbook of Convex Optimization Methods in Imaging Science读者反馈学科排名

GIBE

Introduction,eo content, while preserving the richness of spectral or color information. By some estimates, the global consumer electronics market is poised to be worth a mind-boggling trillion US dollars by 2020.

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d their solutions. Practical considerations such as computational cost, noise containment, and power consumption are introduced as mathematical constraints into the given optimization problem. The chapter concludes with suggestions for future work in this domain.

CHURL

Optimizing Internal Management,maging paradigm, which involves distributing the imaging task between a physical and a computational system and then digitally forming the image datacube of interest from multiplexed measurements by means of solving an inverse problem via convex optimization techniques.

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Computational Spectral and Ultrafast Imaging via Convex Optimization,maging paradigm, which involves distributing the imaging task between a physical and a computational system and then digitally forming the image datacube of interest from multiplexed measurements by means of solving an inverse problem via convex optimization techniques.

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ian perspective on sparse representation-based classification via the introduction of class-specific priors. This formulation represents a consummation of ideas developed for model-based compressive sensing into a general framework for sparse model-based classification.

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nd non-convex objective functions through tractable convex o.This book covers recent advances in image processing and imaging sciences from an optimization viewpoint, especially convex optimization with the goal of designing tractable algorithms. Throughout the handbook, the authors introduce topics