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Depth Perception and Spatial Vision01:15

Depth Perception and Spatial Vision

829
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.
829
Perception01:28

Perception

541
Perception is a fundamental psychological process that enables individuals to organize, interpret, and consciously experience sensory information. This process is crucial for understanding and interacting with the world around us. It includes both bottom-up and top-down processing, each playing a distinct role in how we perceive our environment.
Bottom-up processing begins at the sensory level, where receptors detect external environmental stimuli. These could include the tactile sensation of...
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Parallel Processing01:20

Parallel Processing

205
The brain processes sensory information rapidly due to parallel processing, which involves sending data across multiple neural pathways at the same time. This method allows the brain to manage various sensory qualities, such as shapes, colors, movements, and locations, all concurrently. For instance, when observing a forest landscape, the brain simultaneously processes the movement of leaves, the shapes of trees, the depth between them, and the various shades of green. This enables a quick and...
205
Perceptual Constancy01:12

Perceptual Constancy

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Perceptual constancy is the ability to recognize that objects remain consistent and unchanged even when their appearance varies due to changes in sensory input. There are four main types of perceptual constancy: size constancy, shape constancy, color constancy, and brightness constancy.
Size constancy is the recognition that an object remains the same size, even when its image on the retina changes. For instance, a bus is perceived to be large enough to carry people, even if it looks tiny from...
495
Buoyancy and Stability for Submerged and Floating Bodies01:11

Buoyancy and Stability for Submerged and Floating Bodies

2.0K
In fluid mechanics, buoyancy and stability are key concepts for understanding the behavior of submerged and floating bodies. When a stationary body is fully or partially submerged in a fluid, the fluid exerts a force on the body known as the buoyant force. This force acts vertically upward through a point called the center of buoyancy, which is the center of the displaced fluid volume. According to Archimedes' principle, the magnitude of the buoyant force is equal to the weight of the fluid...
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Super-resolution Fluorescence Microscopy01:37

Super-resolution Fluorescence Microscopy

7.1K
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: Aug 24, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
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Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

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SGUIE-Net: Semantic Attention Guided Underwater Image Enhancement with Multi-Scale Perception.

Qi Qi, Kunqian Li, Haiyong Zheng

    IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
    |October 26, 2022
    PubMed
    Summary

    This study introduces SGUIE-Net, a novel network for underwater image enhancement. It effectively uses semantic information to improve image quality despite limited training data.

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    Area of Science:

    • Computer Vision
    • Image Processing
    • Deep Learning

    Background:

    • Underwater images degrade due to light attenuation, refraction, and scattering, causing color distortion and blur.
    • Training deep learning models for underwater image enhancement is challenging due to a scarcity of paired, high-quality reference images.

    Purpose of the Study:

    • To develop an effective deep learning model for underwater image enhancement using limited paired data.
    • To leverage semantic information for improved, region-specific image restoration.

    Main Methods:

    • Proposed SGUIE-Net, a novel network integrating semantic information for high-level guidance.
    • Introduced a semantic region-wise enhancement module for multi-scale local feature learning.
    • Fused global and local enhancement features for semantically consistent and visually superior results.

    Main Results:

    • SGUIE-Net demonstrated impressive performance on diverse underwater image datasets.
    • The network achieved semantically consistent and visually superior image enhancements.
    • Experimental results validated the effectiveness of the proposed semantic region-wise approach.

    Conclusions:

    • SGUIE-Net offers a robust solution for underwater image enhancement, particularly with limited training data.
    • The integration of semantic information significantly boosts enhancement performance.
    • The proposed method provides a valuable contribution to the field of image restoration.