results: 该论文的结果表明，使用TGV regularization可以提高图像重建的稳定性和准确性，并且可以在不同的图像中实现不同的拟合程度。然而，目前还没有一个可靠的算法来解决TGV regularization的高阶问题。Abstract
Although regularization methods based on derivatives are favored for their robustness and computational simplicity, research exploring higher-order derivatives remains limited. This scarcity can possibly be attributed to the appearance of oscillations in reconstructions when directly generalizing TV-1 to higher orders (3 or more). Addressing this, Bredies et. al introduced a notable approach for generalizing total variation, known as Total Generalized Variation (TGV). This technique introduces a regularization that generates estimates embodying piece-wise polynomial behavior of varying degrees across distinct regions of an image.Importantly, to our current understanding, no sufficiently general algorithm exists for solving TGV regularization for orders beyond 2. This is likely because of two problems: firstly, the problem is complex as TGV regularization is defined as a minimization problem with non-trivial constraints, and secondly, TGV is represented in terms of tensor-fields which is difficult to implement. In this work we tackle the first challenge by giving two simple and implementable representations of n th order TGV
摘要
Real-Time Dynamic Data Driven Deformable Registration for Image-Guided Neurosurgery: Computational Aspects
results: 该方法可以准确地考虑到手术过程中脑组织的变形，并且可以提供高品质的脑MR成像数据，以帮助脑外科手术规划。Abstract
Current neurosurgical procedures utilize medical images of various modalities to enable the precise location of tumors and critical brain structures to plan accurate brain tumor resection. The difficulty of using preoperative images during the surgery is caused by the intra-operative deformation of the brain tissue (brain shift), which introduces discrepancies concerning the preoperative configuration. Intra-operative imaging allows tracking such deformations but cannot fully substitute for the quality of the pre-operative data. Dynamic Data Driven Deformable Non-Rigid Registration (D4NRR) is a complex and time-consuming image processing operation that allows the dynamic adjustment of the pre-operative image data to account for intra-operative brain shift during the surgery. This paper summarizes the computational aspects of a specific adaptive numerical approximation method and its variations for registering brain MRIs. It outlines its evolution over the last 15 years and identifies new directions for the computational aspects of the technique.
摘要
当前的神经外科手术使用各种媒体的医疗图像来准确定位肿瘤和critical brain structures，以便准确肿瘤除除。但是使用前opera的图像在手术过程中具有困难，这是因为脑肿瘤（brain shift）的变形会导致图像与实际状态不符，从而引起差异。实时成像技术可以跟踪这些变形，但是无法完全取代优质的前opera数据。D4NRR是一种复杂且时间消耗的图像处理操作，它允许在手术过程中动态调整前opera图像数据，以适应脑肿瘤的变形。本文概述了D4NRR的计算方面的特点和其变化在过去15年中，以及新的计算方向。
results: 根据这种分析，提出了一种证明性的方法，结合了五个秘密。实验结果显示，新方法与一些最新的方法相当，而与一些较老的方法超越。Abstract
Non-blind image deconvolution has been studied for several decades but most of the existing work focuses on blur instead of noise. In photon-limited conditions, however, the excessive amount of shot noise makes traditional deconvolution algorithms fail. In searching for reasons why these methods fail, we present a systematic analysis of the Poisson non-blind deconvolution algorithms reported in the literature, covering both classical and deep learning methods. We compile a list of five "secrets" highlighting the do's and don'ts when designing algorithms. Based on this analysis, we build a proof-of-concept method by combining the five secrets. We find that the new method performs on par with some of the latest methods while outperforming some older ones.
摘要
非盲图像恢复已经在数十年中被研究，但大多数现有工作都集中在模糊问题上，而在光子限制条件下，过度的射击噪声使传统的恢复算法失效。在寻找这些方法失败的原因时，我们提供了系统性的文献分析，涵盖了经典和深度学习方法。我们编译了五个"秘密"，描述了设计算法时的做法和不做法。基于这一分析，我们建立了一种证明性方法，并发现其与一些最新的方法性能相似，而在一些较老的方法上表现出色。
A flexible and accurate total variation and cascaded denoisers-based image reconstruction algorithm for hyperspectrally compressed ultrafast photography
results: 实验和 simulate结果表明，提议的TV-CD算法可以有效提高HCUP图像重建的准确性和质量，并且可以推动HCUP在捕捉高维复杂物理、化学和生物ultrafast光学场景中的实际应用。Abstract
Hyperspectrally compressed ultrafast photography (HCUP) based on compressed sensing and the time- and spectrum-to-space mappings can simultaneously realize the temporal and spectral imaging of non-repeatable or difficult-to-repeat transient events passively in a single exposure. It possesses an incredibly high frame rate of tens of trillions of frames per second and a sequence depth of several hundred, and plays a revolutionary role in single-shot ultrafast optical imaging. However, due to the ultra-high data compression ratio induced by the extremely large sequence depth as well as the limited fidelities of traditional reconstruction algorithms over the reconstruction process, HCUP suffers from a poor image reconstruction quality and fails to capture fine structures in complex transient scenes. To overcome these restrictions, we propose a flexible image reconstruction algorithm based on the total variation (TV) and cascaded denoisers (CD) for HCUP, named the TV-CD algorithm. It applies the TV denoising model cascaded with several advanced deep learning-based denoising models in the iterative plug-and-play alternating direction method of multipliers framework, which can preserve the image smoothness while utilizing the deep denoising networks to obtain more priori, and thus solving the common sparsity representation problem in local similarity and motion compensation. Both simulation and experimental results show that the proposed TV-CD algorithm can effectively improve the image reconstruction accuracy and quality of HCUP, and further promote the practical applications of HCUP in capturing high-dimensional complex physical, chemical and biological ultrafast optical scenes.
摘要
高级спектраль压缩超快摄影（HCUP）基于压缩感知和时间-频谱空间映射，可同时实现非重复或Difficult-to-repeat脉冲事件的时间和频谱成像，在单 exposure 中完成。它具有无 precedent 的高帧率，达到了 tens of trillions of frames per second 和 Several hundred sequence depth，扮演了革命性的角色在单shot ultrafast optical imaging 中。然而，由于 ultra-high data compression ratio 以及传统重建算法的限制，HCUP 的图像重建质量受到了严重的限制，无法捕捉复杂的脉冲场景中的细节。为解决这些限制，我们提出了基于全量变量（TV）和级联去噪器（CD）的 flexible image reconstruction algorithm，称为 TV-CD 算法。它在 iterative plug-and-play alternating direction method of multipliers 框架中，将 TV 去噪模型与多种高级深度学习去噪模型相互嵌套，以保持图像的稳定性，同时利用深度去噪网络获取更多的PRIOR，解决了 мест similarity和运动补做的共同简约表示问题。在 simulate 和实验中，我们发现 TV-CD 算法可以有效提高 HCUP 的图像重建质量和精度，并推动 HCUP 在捕捉高维复杂物理、化学和生物 ultrafast optical 场景中的实际应用。
For: This paper provides an overview of the emerging research frontiers in photoacoustic imaging (PAI) technology, including its applications in various biomedical fields and its combination with other advanced technologies.* Methods: The paper discusses the current state of PAI technology and its combination with other technologies, including PAI plus treatment, PAI plus new circuit design, PAI plus accurate positioning systems, PAI plus fast scanning systems, PAI plus novel ultrasound sensors, PAI plus advanced laser sources, PAI plus deep learning, and PAI plus other imaging modalities.* Results: The paper summarizes the technical advantages and prospects for application of each technology, with a focus on recent developments in the past three years. It also discusses the challenges and potential future work in the PAI plus X area.Here’s the information in Simplified Chinese text:* For: 这篇评论文章提供了photoacoustic imaging（PAI）技术的新兴研究前沿，包括它在各种生物医学领域的应用以及与其他先进技术的组合。* Methods: 文章讨论了PAI技术的当前状况以及它与其他技术的组合，包括PAI加 treatment、PAI加新电路设计、PAI加精准定位系统、PAI加快扫描系统、PAI加新式ultrasound探测器、PAI加高级激光源、PAI加深度学习以及PAI加其他成像模式。* Results: 文章summarizes each technology’s current state, technical advantages, and prospects for application, with a focus on recent developments in the past three years. It also discusses the challenges and potential future work in the PAI plus X area.Abstract
Photoacoustic imaging (PAI) is a novel modality in biomedical imaging technology that combines the rich optical contrast with the deep penetration of ultrasound. To date, PAI technology has found applications in various biomedical fields. In this review, we present an overview of the emerging research frontiers on PAI plus other advanced technologies, named as PAI plus X, which includes but not limited to PAI plus treatment, PAI plus new circuits design, PAI plus accurate positioning system, PAI plus fast scanning systems, PAI plus novel ultrasound sensors, PAI plus advanced laser sources, PAI plus deep learning, and PAI plus other imaging modalities. We will discuss each technology's current state, technical advantages, and prospects for application, reported mostly in recent three years. Lastly, we discuss and summarize the challenges and potential future work in PAI plus X area.
摘要
照片听影技术（PAI）是生物医学成像技术中的一种新兴方式，它结合了丰富的光学强度和深入的超声探测。至今，PAI技术已找到了多个生物医学应用领域。在本文中，我们提供PAI以外其他高级技术的概述，称为PAI加X，包括但不限于PAI加治疗、PAI加新电路设计、PAI加准确定位系统、PAI加快扫描系统、PAI加新的ultrasound探测器、PAI加高级激光源、PAI加深度学习和PAI加其他成像方式。我们将讨论每种技术的当前状态、技术优势和应用前景，大多是在过去三年内发表的研究报告。最后，我们讨论和总结PAI加X领域中的挑战和未来工作。