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Vision01:24

Vision

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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.
52.2K
Super-resolution Fluorescence Microscopy01:37

Super-resolution Fluorescence Microscopy

6.7K
Super-resolution fluorescence microscopy (SRFM) provides a better resolution than conventional fluorescence microscopy by reducing the point spread function (PSF). PSF is the light intensity distribution from a point that causes it to appear blurred. Due to PSF, each fluorescing point appears bigger than its actual size, and it is the PSF interference of nearby fluorophores that causes the blurred image. Various approaches to achieving higher resolution through SRFM have recently been...
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Related Experiment Video

Updated: Apr 10, 2026

Lensless Fluorescent Microscopy on a Chip
11:23

Lensless Fluorescent Microscopy on a Chip

Published on: August 17, 2011

18.4K

Squeeze-EnGAN: Memory Efficient and Unsupervised Low-Light Image Enhancement for Intelligent Vehicles.

Haegyo In1, Juhum Kweon2, Changjoo Moon1

  • 1Department of Smart Vehicle Engineering, Konkuk University, Seoul 05029, Republic of Korea.

Sensors (Basel, Switzerland)
|April 28, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces Squeeze-EnGAN, a novel deep learning method for enhancing low-light images without paired data. The model improves object detection for intelligent vehicles, offering real-time performance and efficiency.

Keywords:
autonomous drivinggenerative adversarial networklow-light image enhancementunsupervised learning

Related Experiment Videos

Last Updated: Apr 10, 2026

Lensless Fluorescent Microscopy on a Chip
11:23

Lensless Fluorescent Microscopy on a Chip

Published on: August 17, 2011

18.4K

Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Intelligent Transportation Systems

Background:

  • Autonomous vehicles rely on sensors like RGB cameras, which perform poorly in low light.
  • Existing low-light image enhancement (LLIE) methods are often costly or struggle with road scenes.
  • Supervised LLIE methods require paired datasets, which are difficult to obtain for driving scenarios.

Purpose of the Study:

  • To develop a memory-efficient, unsupervised LLIE method for intelligent vehicles.
  • To address the limitations of existing LLIE models in adapting to road scenes and the lack of paired datasets.

Main Methods:

  • Proposed Squeeze-EnGAN, a Generative Adversarial Network (GAN)-based LLIE method.
  • Incorporated a fire module into a U-net architecture for reduced parameters and computational cost.
  • Utilized an unsupervised learning approach, eliminating the need for paired low-light and normal-light datasets.

Main Results:

  • Squeeze-EnGAN demonstrated significant memory efficiency and reduced Multiply-Accumulate Operations (MACs) compared to EnlightenGAN.
  • Achieved real-time performance on embedded systems like Jetson Xavier.
  • Enhanced images from Squeeze-EnGAN improved object detection accuracy for intelligent vehicles.

Conclusions:

  • Squeeze-EnGAN offers an effective and efficient solution for low-light image enhancement in intelligent vehicles.
  • The unsupervised approach overcomes the challenge of acquiring paired datasets for driving scenes.
  • The model's ability to improve object detection highlights its potential for enhancing autonomous driving systems.