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相关概念视频

Vision01:24

Vision

52.4K
Vision is the result of light being detected and transduced into neural signals by the retina of the eye. This information is then further analyzed and interpreted by the brain. First, light enters the front of the eye and is focused by the cornea and lens onto the retina—a thin sheet of neural tissue lining the back of the eye. Because of refraction through the convex lens of the eye, images are projected onto the retina upside-down and reversed.
52.4K
Deconvolution01:20

Deconvolution

116
Deconvolution, also known as inverse filtering, is the process of extracting the impulse response from known input and output signals. This technique is vital in scenarios where the system's characteristics are unknown, and they must be inferred from the observable signals.
Deconvolution involves several mathematical techniques to derive the impulse response. One common approach is polynomial division. In this method, the input and output sequences are treated as coefficients of...
116
Visual System01:26

Visual System

438
Light enters the eye through the cornea, a transparent, dome-shaped surface covering the surface of the eyeball that helps to direct and focus incoming light. This light is then channeled toward the pupil, an adjustable opening whose size is controlled by the iris. The iris, a pigmented muscle, regulates the amount of light entering the eye by contracting or dilating the pupil, thereby ensuring optimal light levels for clear vision.
Once through the pupil, the light passes through the lens, a...
438
Convolution: Math, Graphics, and Discrete Signals01:24

Convolution: Math, Graphics, and Discrete Signals

210
In any LTI (Linear Time-Invariant) system, the convolution of two signals is denoted using a convolution operator, assuming all initial conditions are zero. The convolution integral can be divided into two parts: the zero-input or natural response and the zero-state or forced response, with t0 indicating the initial time.
To simplify the convolution integral, it is assumed that both the input signal and impulse response are zero for negative time values. The graphical convolution process...
210
Convolution Properties II01:17

Convolution Properties II

147
The important convolution properties include width, area, differentiation, and integration properties.
The width property indicates that if the durations of input signals are T1 and T2, then the width of the output response equals the sum of both durations, irrespective of the shapes of the two functions. For instance, convolving two rectangular pulses with durations of 2 seconds and 1 second results in a function with a width of 3 seconds.
The area property asserts that the area under the...
147
Convolution Properties I01:20

Convolution Properties I

120
Convolution computations can be simplified by utilizing their inherent properties.
The commutative property reveals that the input and the impulse response of an LTI (Linear Time-Invariant) system can be interchanged without affecting the output:
120

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Using Light Sheet Fluorescence Microscopy to Image Zebrafish Eye Development
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在波编码系统中,编码图像的盲解卷网络.

J M Reyes-Alfaro, C Toxqui-Quitl, A Padilla-Vivanco

    Optics express
    |April 12, 2025
    PubMed
    概括

    这项研究引入了使用Jacobbi-Fourier函数和卷积神经网络 (CNN) 进行盲目解卷的光学数字成像系统. 该系统有效地恢复高频率的图像失焦,即使在未经训练的光学条件下.

    科学领域:

    • 光学成像技术的使用.
    • 数字图像处理是数字图像处理.
    • 机器学习是机器学习.

    背景情况:

    • 传统的解卷方法与未知的光学传输函数 (OTF) 斗争,需要大量的预处理.
    • 失焦偏差显著降低了光学系统中的图像质量.
    • 阶段罩 (PM) 提供了一种在光学系统中编码信息的方法.

    研究的目的:

    • 提出和验证一个光学数字成像系统用于盲人解卷,使用Jacobi-Fourier配置函数和卷积神经网络 (CNN).
    • 证明系统能够在失焦条件下恢复高频图像细节,而无需事先了解光学传输函数 (OTF).
    • 评估系统在未经训练的编码图像上的性能及其对光学参数变化的稳定性.

    主要方法:

    • 使用点扩展函数 (PSF) 来编码图像,该函数来自立方和雅科比-弗里耶多项式 (JFP) 阶段面罩 (PM),其辐射顺序为p=7,9和10.
    • 在模拟编码图像数据集上训练一个盲目的解卷卷积神经网络 (CNN),添加了失焦偏移.
    • 将受过训练的CNN应用于模拟和实验的光数字成像数据,包括在训练集中不存在的PMs编码的图像.

    主要成果:

    • 经过训练的CNN成功地恢复了不同失焦值的图像中的高频率,超过了传统方法.
    • 该系统证明了有效的盲目解卷,而不需要预处理步骤,如消除噪音或对实验数据进行放射性规范化.

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  • 美国有线电视新闻网 (CNN) 从未经过训练的相罩中解码了光学编码的图像,展示了适应性和强度.
  • 拟议的CNN架构实现了高质量的图像恢复,最小的工件和增强的对比度,即使在显著的失焦下.

    • 结论:

    • 拟议的光学数字成像系统与基于CNN的盲解卷网络提供了一个强大的解决方案,用于在失焦的情况下恢复图像.
    • 该系统能够处理未经训练的光学条件并消除预处理的需要,这使得它对现实世界的应用非常实用.
    • 这种方法提供了一个计算效率高的方法来提高光学系统中的图像质量,而无需对光学传输函数 (OTF) 的精确知识.