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Related Concept Videos

Depth Perception and Spatial Vision01:15

Depth Perception and Spatial Vision

Depth perception is the ability to perceive objects three-dimensionally. It relies on two types of cues: binocular and monocular. Binocular cues depend on the combination of images from both eyes and how the eyes work together. Since the eyes are in slightly different positions, each eye captures a slightly different image. This disparity between images, known as binocular disparity, helps the brain interpret depth. When the brain compares these images, it determines the distance to an object.
Light Acquisition02:16

Light Acquisition

In order to produce glucose, plants need to capture sufficient light energy. Many modern plants have evolved leaves specialized for light acquisition. Leaves can be only millimeters in width or tens of meters wide, depending on the environment. Due to competition for sunlight, evolution has driven the evolution of increasingly larger leaves and taller plants, to avoid shading by their neighbors with contaminant elaboration of root architecture and mechanisms to transport water and nutrients.

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Related Experiment Video

Updated: May 28, 2026

Digital Inline Holographic Microscopy (DIHM) of Weakly-scattering Subjects
10:16

Digital Inline Holographic Microscopy (DIHM) of Weakly-scattering Subjects

Published on: February 8, 2014

Low-Light Monocular Depth Estimation Algorithm Based on Illumination Adaptive Image Enhancement.

Xiaoqian Cao1, Yang Wang2, Wanyu Li1

  • 1School of Electronic and Control Engineering, Shaanxi University of Science and Technology, Xi'an 710021, China.

Sensors (Basel, Switzerland)
|May 27, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces an illumination adaptive image enhancement-based low-light depth estimation algorithm (IAIE_LDE) to address challenges in low-light environments. The novel approach effectively corrects inconsistent illumination, improving depth map accuracy for monocular depth estimation.

Keywords:
depth estimationillumination inconsistent correctionimage enhancementlow-light scene

Related Experiment Videos

Last Updated: May 28, 2026

Digital Inline Holographic Microscopy (DIHM) of Weakly-scattering Subjects
10:16

Digital Inline Holographic Microscopy (DIHM) of Weakly-scattering Subjects

Published on: February 8, 2014

Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Image Processing

Background:

  • Monocular depth estimation in low-light conditions presents significant challenges.
  • Existing algorithms often struggle with inconsistent illumination, leading to edge blurring and depth holes.

Purpose of the Study:

  • To propose an illumination adaptive image enhancement-based low-light depth estimation algorithm (IAIE_LDE).
  • To address the issue of inconsistent illumination in low-light depth estimation.

Main Methods:

  • Developed an S-shaped illumination estimation basis illumination adaptive consistent correction model.
  • Constructed a three-module architecture: illumination adaptive correction, low-light image enhancement (using EnlightGAN), and depth estimation (using ZoeDepth).
  • Calculated pixel-wise correction coefficients based on estimated illumination to mitigate inconsistency.

Main Results:

  • The proposed IAIE_LDE algorithm effectively eliminates edge blurring and depth hole effects caused by inconsistent lighting.
  • Achieved superior qualitative and quantitative performance compared to state-of-the-art methods on Oxford RobotCar and nuScenes datasets.
  • Demonstrated the ability to produce depth maps comparable to those from high-quality illuminated images.

Conclusions:

  • The IAIE_LDE algorithm offers a robust solution for monocular depth estimation in challenging low-light scenarios.
  • The illumination adaptive correction model is key to overcoming inconsistent lighting issues.
  • The integrated approach enhances overall depth estimation accuracy and reliability under adverse lighting conditions.