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Upsampling01:22

Upsampling

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Managing signal sampling rates is essential in digital signal processing to maintain signal integrity. A decimated signal, characterized by a reduced frequency range due to its lower sampling rate, can be upsampled by inserting zeros between each sample. This upsampling process expands the original spectrum and introduces repeated spectral replicas at intervals dictated by the new Nyquist frequency. To refine this zero-inserted sequence, it is passed through a lowpass filter with a cutoff...
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Imaging Biological Samples with Optical Microscopy01:18

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Optical microscopy uses optic principles to provide detailed images of samples. Antonie van Leeuwenhoek designed the first compound optical microscope in the 17th century to visualize blood cells, bacteria, and yeast cells. In 1830, Joseph Jackson Lister created an essentially modern light microscope. The 20th century saw the development of microscopes with enhanced magnification and resolution.
In optical microscopy, the specimen to be viewed is placed on a glass slide and clipped on the stage...
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Downsampling01:20

Downsampling

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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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Extending Camera's Capabilities in Low Light Conditions Based on LIP Enhancement Coupled with CNN Denoising.

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Updated: Jun 17, 2025

A New Technique for Quantitative Analysis of Hair Loss in Mice Using Grayscale Analysis
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Image Enhancement Thanks to Negative Grey Levels in the Logarithmic Image Processing Framework.

Michel Jourlin1

  • 1Laboratoire Hubert Curien, UMR CNRS 5516, 18 Rue Professeur Benoît Lauras, 42000 Saint-Étienne, France.

Sensors (Basel, Switzerland)
|August 10, 2024
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This study introduces a novel image enhancement technique using the Logarithmic Image Processing (LIP) framework to improve low-light images. The method extends dynamic range in real-time, enhancing visual quality with physical justification.

Keywords:
enhancementfull dynamic rangeimagelogarithmic image processingnegative grey level

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Area of Science:

  • Image Processing
  • Computer Vision
  • Human Visual System Modeling

Background:

  • Image enhancement is crucial in image processing, often focusing on perceived quality.
  • Existing methods lack strong physical justification and human visual system modeling.

Purpose of the Study:

  • To propose a physically justified image enhancement approach modeling the human visual system.
  • To extend the dynamic range of low-light images using the Logarithmic Image Processing (LIP) framework.

Main Methods:

  • Utilizing the Logarithmic Image Processing (LIP) framework.
  • Introducing negative grey levels as light intensifiers within the LIP model.
  • Extending dynamic range for low-light images in real-time.

Main Results:

  • Successful extension of low-light image dynamic range to the full grey scale.
  • Real-time image enhancement at camera speed.
  • Method demonstrated to be reversible and generalizable to color and reflection images.

Conclusions:

  • The proposed LIP-based approach offers a physically justified and effective method for image enhancement.
  • The technique successfully models the human visual system and enhances low-light images in real-time.
  • The reversibility and generalizability of the method open avenues for diverse applications in image processing.