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Downsampling

Downsampling

When considering a sampled sequence with zero values between sampling instants, one can replace it by taking every N-th value of the sequence. At these integer multiples of N, the original and sampled sequences coincide. This process, known as decimation, involves extracting every N-th sample from a sequence, thereby creating a more efficient sequence.
The Fourier transform of the decimated sequence reveals a combination of scaled and shifted versions of the original spectrum. This...

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X-ray Dose Reduction through Adaptive Exposure in Fluoroscopic Imaging

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Published on: September 11, 2011

Quantitative evaluation of a deep learning-based noise reduction algorithm in digital radiography using noise power

Sho Maruyama¹, Hiroki Saitou²

¹Department of Radiological Technology, Gunma Prefectural College of Health Sciences, Maebashi, Gunma, Japan.

Journal of Applied Clinical Medical Physics

|February 25, 2026

Summary

Deep learning noise reduction (DLNR) in digital radiography (DR) shows superior performance at low doses compared to conventional methods. Detailed frequency analysis reveals distinct noise suppression behaviors, highlighting the need for dose-dependent evaluation.

Keywords:

deep learning‐based noise reduction digital radiography dose‐dependent performance frequency‐dependent assessment noise power spectrum

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Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

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Published on: November 30, 2022

Area of Science:

Medical Imaging
Digital Radiography
Image Processing

Background:

Deep learning (DL)-based noise reduction (DLNR) is increasingly used in clinical digital radiography (DR) systems.
DLNR algorithms offer superior performance over conventional methods but function as "black boxes" with nonlinear behavior.
Understanding the impact of DLNR on image quality across various imaging conditions is crucial.

Purpose of the Study:

To quantitatively evaluate the image quality of a commercial DLNR algorithm, intelligent noise reduction (INR), in DR.
To compare INR's noise reduction performance against a conventional rule-based algorithm (Con-NR).
To utilize frequency-domain metrics, specifically noise power spectrum (NPS) analysis, for detailed comparison.

Main Methods:

Noise power spectrum (NPS) analysis was employed to assess spatial-frequency-dependent noise characteristics.
Both INR and Con-NR were evaluated across varying radiation dose levels and different imaging objects.
A novel metric, the NPS improvement factor (NPSIF), was introduced to quantify noise suppression across frequency ranges.

Main Results:

The DL-based INR algorithm demonstrated significant noise reduction at low radiation doses compared to Con-NR.
The performance advantage of INR diminished at higher dose levels.
The NPSIF effectively highlighted frequency-specific differences, revealing the distinct strengths and limitations of each noise reduction technique.

Conclusions:

The dose-dependent performance of DL-based INR suggests its sensitivity to the training data characteristics.
Distinct differences in noise suppression behavior exist between DL-based and conventional methods in DR.
Frequency-domain analysis is essential for understanding advanced image processing techniques in DR; further research should integrate noise analysis with diagnostic performance.