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Related Concept Videos

Regression Toward the Mean01:52

Regression Toward the Mean

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Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when...
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Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
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Residuals and Least-Squares Property01:11

Residuals and Least-Squares Property

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The vertical distance between the actual value of y and the estimated value of y. In other words, it measures the vertical distance between the actual data point and the predicted point on the line
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A calibration curve is a plot of the instrument's response against a series of known concentrations of a substance. This curve is used to set the instrument response levels, using the substance and its concentrations as standards. Alternatively, or additionally, an equation is fitted to the calibration curve plot and subsequently used to calculate the unknown concentrations of other samples reliably.
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In a nonhomogeneous rod made up of steel and brass, restrained at both ends and subjected to a temperature change, several steps are involved in calculating the stress and compressive load. Due to the problem's static indeterminacy, one end support is disconnected, allowing the rod to experience the temperature change freely. Next, an unknown force is applied at the free end, triggering deformations in the rod's steel and brass portions. These deformations are then calculated and added...
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Deconvolution01:20

Deconvolution

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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.
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Related Experiment Video

Updated: Jul 19, 2026

Subsurface Defect Localization by Structured Heating Using Laser Projected Photothermal Thermography
11:34

Subsurface Defect Localization by Structured Heating Using Laser Projected Photothermal Thermography

Published on: May 15, 2017

LPATR-Net: Learnable Piecewise Affine Transformation Regression Assisted Data-Driven Dehazing Framework.

Yuelong Li, Fei Chen, Zhenwei Liu

    IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
    |December 5, 2025
    PubMed
    Summary

    This study introduces LPATR-Net, a novel image dehazing framework that suppresses faulty training data by reducing fitting flexibility. This approach enhances robustness without manual labeling, integrating traditional regression with deep learning for improved performance.

    Related Experiment Videos

    Last Updated: Jul 19, 2026

    Subsurface Defect Localization by Structured Heating Using Laser Projected Photothermal Thermography
    11:34

    Subsurface Defect Localization by Structured Heating Using Laser Projected Photothermal Thermography

    Published on: May 15, 2017

    Area of Science:

    • Computer Vision
    • Artificial Intelligence
    • Image Processing

    Background:

    • Deep neural networks (DNNs) dominate image dehazing, relying on paired training data.
    • Real-world haze data often contains imperfect ground truth (GT) samples, challenging supervised learning.
    • Existing methods struggle with faulty GT, limiting robustness in natural image dehazing.

    Purpose of the Study:

    • To develop a robust image dehazing framework resistant to imperfect ground truth data.
    • To introduce a novel approach that intentionally limits fitting flexibility for enhanced robustness.
    • To integrate traditional regression techniques with deep learning for improved dehazing performance.

    Main Methods:

    • Proposed LPATR-Net framework with a fitting power suppression mechanism.
    • Utilized fitting-restrained learnable piecewise affine transformation regression.
    • Integrated a custom multi-concern, high-accuracy dehazing fitting companion (All-Mattering).

    Main Results:

    • LPATR-Net effectively suppresses interference from minority unqualified GT samples.
    • The framework achieves a seamless integration of regression and deep learning.
    • Extensive experiments on five public datasets verified the effectiveness and transplantability of the core regression structure.

    Conclusions:

    • LPATR-Net offers a robust solution for image dehazing in the presence of faulty training data.
    • The approach demonstrates the benefit of controlled fitting flexibility for improved model robustness.
    • The proposed regression structure shows wide-ranging applicability in image dehazing tasks.