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Related Concept Videos

Deconvolution01:20

Deconvolution

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Deconvolution, also known as inverse filtering, is the process of extracting the impulse response from known input and output signals. This technique is vital in scenarios where the system's characteristics are unknown, and they must be inferred from the observable signals.
Deconvolution involves several mathematical techniques to derive the impulse response. One common approach is polynomial division. In this method, the input and output sequences are treated as coefficients of...
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Depth Perception and Spatial Vision01:15

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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.
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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.
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Related Experiment Video

Updated: Jun 30, 2025

Assessing Binocular Central Visual Field and Binocular Eye Movements in a Dichoptic Viewing Condition
07:45

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Published on: July 21, 2020

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Depth-Aware Unpaired Video Dehazing.

Yang Yang, Chun-Le Guo, Xiaojie Guo

    IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
    |March 22, 2024
    PubMed
    Summary

    This study introduces a new unpaired video dehazing method using depth information. It improves temporal consistency and haze removal effectiveness for practical applications.

    Area of Science:

    • Computer Vision
    • Image Processing

    Background:

    • Unpaired video dehazing is challenging due to the lack of paired data.
    • Key issues include maintaining temporal consistency and enhancing dehazing performance.

    Purpose of the Study:

    • To develop a novel unpaired video dehazing framework.
    • To address temporal consistency and dehazing ability using depth information.

    Main Methods:

    • Synthesizing realistic motions with depth information to enhance temporal losses.
    • Utilizing depth information for adversarial learning with a depth-aware local discriminator.
    • Constructing additional regularization and supervision through depth information.

    Main Results:

    • Improved spatiotemporal consistency in dehazed videos.

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  • Enhanced haze removal by guiding the discriminator to focus on residual haze regions.
  • Demonstrated effectiveness and superiority over existing methods through extensive experiments.

    • Conclusions:

    • The proposed framework offers a practical solution for unpaired video dehazing.
    • Depth information is crucial for improving both temporal consistency and dehazing accuracy.
    • This work represents the initial exploration of unpaired video dehazing.