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Deconvolution

Deconvolution

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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Digital Inline Holographic Microscopy DIHM of Weakly-scattering Subjects

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Unsupervised single-image dehazing using the multiple-scattering model.

Shunmin An, Xixia Huang, Linling Wang

|October 6, 2021

Summary

This summary is machine-generated.

This study introduces an unsupervised image dehazing method using a multiple scattering model. It effectively removes haze from single images without needing large datasets or intensive computation.

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Last Updated: Oct 17, 2025

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Area of Science:

Computer Vision
Image Processing
Artificial Intelligence

Background:

Single-image dehazing is challenging due to complex atmospheric conditions.
Existing methods often require large datasets or supervised learning, limiting their applicability.

Purpose of the Study:

To propose an unsupervised single-image dehazing method.
To leverage a multiple scattering model and unsupervised learning for efficient dehazing.

Main Methods:

Utilizes an undegraded atmospheric multiple scattering model.
Employs unsupervised neural networks to avoid intensive dataset operations.
Incorporates three unsupervised learning branches and a blur kernel estimation module.
Constructs an unsupervised loss function using prior knowledge for constraint.

Main Results:

Successfully implements dehazing on single real-world images.
Demonstrates good performance in image dehazing compared to state-of-the-art methods.
Avoids the influence of multiple scattering and reduces computational load.

Conclusions:

The proposed unsupervised method offers an effective solution for single-image dehazing.
It provides a computationally efficient alternative to supervised approaches.
The method shows promise for practical applications in various imaging scenarios.