標(biāo)題: Titlebook: Handbook of Convex Optimization Methods in Imaging Science; Vishal Monga Book 2018 Springer International Publishing AG 2018 Optimization. [打印本頁(yè)] 作者: cessation 時(shí)間: 2025-3-21 19:11
書(shū)目名稱Handbook of Convex Optimization Methods in Imaging Science影響因子(影響力)
書(shū)目名稱Handbook of Convex Optimization Methods in Imaging Science影響因子(影響力)學(xué)科排名
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書(shū)目名稱Handbook of Convex Optimization Methods in Imaging Science被引頻次
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書(shū)目名稱Handbook of Convex Optimization Methods in Imaging Science年度引用學(xué)科排名
書(shū)目名稱Handbook of Convex Optimization Methods in Imaging Science讀者反饋
書(shū)目名稱Handbook of Convex Optimization Methods in Imaging Science讀者反饋學(xué)科排名
作者: GIBE 時(shí)間: 2025-3-21 21:21
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.作者: 不成比例 時(shí)間: 2025-3-22 01:00 作者: Adenocarcinoma 時(shí)間: 2025-3-22 05:48
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.作者: CHURL 時(shí)間: 2025-3-22 10:52
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.作者: nurture 時(shí)間: 2025-3-22 12:52 作者: 遭遇 時(shí)間: 2025-3-22 20:13
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.作者: Hectic 時(shí)間: 2025-3-23 00:39
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.作者: myopia 時(shí)間: 2025-3-23 02:46 作者: Osteoarthritis 時(shí)間: 2025-3-23 05:45
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作者: triptans 時(shí)間: 2025-3-23 12:15
,Ageing Population: What’s New?,terministic and Bayesian frameworks. These methods offer substantial improvements in image quality, suppression of noise and clutter. Analytic methods also have the advantage of computational efficiency.作者: Cardiac 時(shí)間: 2025-3-23 16:10
Book 2018l of designing tractable algorithms. Throughout the handbook, the authors introduce topics on the most key aspects of image acquisition and processing that are based on the formulation and solution of novel optimization problems. The first part includes a review of the mathematical methods and found作者: 小卒 時(shí)間: 2025-3-23 19:35
Book 2018t to image understanding. Throughout, convex optimization techniques are shown to be a criticallyimportant mathematical tool for imaging science problems and applied extensively...Convex Optimization Methods in Imaging Science .is the first book of its kind and will appeal to undergraduate and gradu作者: heterodox 時(shí)間: 2025-3-23 22:23 作者: 鞭子 時(shí)間: 2025-3-24 05:01
Sparsity Based Nonlocal Image Restoration: An Alternating Optimization Approach,on and divide-and-conquer—even though they do not help the pursuit of a globally optimal solution—are often sufficient for the applications of image restoration. We will use two specific applications—namely image denoising and compressed sensing—to demonstrate how simultaneous sparse coding and nonl作者: happiness 時(shí)間: 2025-3-24 09:31 作者: 懸掛 時(shí)間: 2025-3-24 10:43
Handbook of Convex Optimization Methods in Imaging Science作者: Substitution 時(shí)間: 2025-3-24 15:08 作者: excursion 時(shí)間: 2025-3-24 19:06
ticallyimportant mathematical tool for imaging science problems and applied extensively...Convex Optimization Methods in Imaging Science .is the first book of its kind and will appeal to undergraduate and gradu978-3-319-87121-9978-3-319-61609-4作者: 印第安人 時(shí)間: 2025-3-25 03:15
Understanding China‘sOvercapacityceptance owing to its high performance and low complexity, is the representative image quality assessment model that is studied. Specifically, a detailed exposition of the mathematical properties of the SSIM index is presented first, followed by a discussion on the design of linear and non-linear SS作者: 六邊形 時(shí)間: 2025-3-25 06:30
Work Teams in Chinese Enterprises,on and divide-and-conquer—even though they do not help the pursuit of a globally optimal solution—are often sufficient for the applications of image restoration. We will use two specific applications—namely image denoising and compressed sensing—to demonstrate how simultaneous sparse coding and nonl作者: SOBER 時(shí)間: 2025-3-25 09:52 作者: 你敢命令 時(shí)間: 2025-3-25 12:11 作者: 有害 時(shí)間: 2025-3-25 18:52 作者: Oligarchy 時(shí)間: 2025-3-25 21:37
978-3-319-87121-9Springer International Publishing AG 2018作者: 相一致 時(shí)間: 2025-3-26 03:12 作者: 動(dòng)作謎 時(shí)間: 2025-3-26 08:05
Vishal Mongaestimation for image processing and computer vision etc.Provides insight on handling real-world imaging science problems that involve hard and non-convex objective functions through tractable convex o作者: 紋章 時(shí)間: 2025-3-26 08:37
http://image.papertrans.cn/h/image/421102.jpg作者: hangdog 時(shí)間: 2025-3-26 15:19 作者: Parameter 時(shí)間: 2025-3-26 20:33
Optimizing Image Quality,. A vast majority of these multimedia services are consumer-centric and therefore must guarantee a certain level of perceptual quality. Given the massive volumes of image and video data in question, it is only natural to adopt automatic quality prediction and optimization tools. The past decade has 作者: 收養(yǎng) 時(shí)間: 2025-3-26 23:06 作者: Forehead-Lift 時(shí)間: 2025-3-27 02:17 作者: Ambulatory 時(shí)間: 2025-3-27 08:28 作者: Apoptosis 時(shí)間: 2025-3-27 11:05 作者: 平常 時(shí)間: 2025-3-27 16:03 作者: Permanent 時(shí)間: 2025-3-27 21:07 作者: indignant 時(shí)間: 2025-3-28 00:40
Optimization Problems Associated with Manifold-Valued Curves with Applications in Computer Vision,o operate in resource constrained environments. We address these concerns in this chapter, by proposing a dictionary learning scheme that takes geometry and time into account, while performing better than the original data in applications such as activity recognition. We are able to do this with the作者: 取之不竭 時(shí)間: 2025-3-28 04:03 作者: Adornment 時(shí)間: 2025-3-28 07:37 作者: mettlesome 時(shí)間: 2025-3-28 13:49 作者: dainty 時(shí)間: 2025-3-28 14:44
,Ageing Population: What’s New?,wed as constrained least squares problems exploiting sparsity. We reviewed analytic and large scale numerical optimization based approaches in both deterministic and Bayesian frameworks. These methods offer substantial improvements in image quality, suppression of noise and clutter. Analytic methods作者: Jargon 時(shí)間: 2025-3-28 22:03
Optimizing Internal Management,ith widespread applications in diverse domains. Due to the intrinsic limitation of two-dimensional detectors in capturing inherently higher-dimensional data, multidimensional imaging techniques conventionally rely on a scanning process, which renders them inefficient in terms of light throughput and作者: Instrumental 時(shí)間: 2025-3-29 02:41
Guobin Xu,Yanhui Chen,Lianhua Xuin non-traditional ways. An area of promise for these theories is object recognition. In this chapter, we review the role of algorithms based on SR and DL for object recognition. In particular, supervised, unsupervised, weakly supervised, nonlinear kernel-based, convolutional sparse coding and analy作者: 從屬 時(shí)間: 2025-3-29 06:38
Work Teams in Chinese Enterprises,estions remain open—e.g., how to translate some physical insight into an appropriate mathematical objective/cost functional? what kind of optimization tools should be called on first? The objective of this chapter is to stress the difference between the theory and the practice—namely, in the practic作者: 費(fèi)解 時(shí)間: 2025-3-29 09:44 作者: Ascendancy 時(shí)間: 2025-3-29 13:49
https://doi.org/10.1007/978-3-030-33938-8o operate in resource constrained environments. We address these concerns in this chapter, by proposing a dictionary learning scheme that takes geometry and time into account, while performing better than the original data in applications such as activity recognition. We are able to do this with the作者: 臭名昭著 時(shí)間: 2025-3-29 18:23
arious disciplines as well as?industry professionals. As with all volumes in Springer’s Major Reference Works program, readers will benefit from access to a continually updated online version. .978-3-030-48652-5
