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Reflection of Waves

Reflection of Waves

When a wave travels from one medium to another, it gets reflected at the boundary of the second medium. A common example of this is when a person yells at a distance from a cliff and hears the echo of their voice. The sound waves (longitudinal waves) traveling in the air are reflected from the bounding cliff. Similarly, flipping one end of a string whose other end is tied to a wall causes a pulse (transverse wave) to travel through the string, which gets reflected upon reaching the wall. In...

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Related Experiment Video

Updated: Apr 28, 2026

Measuring Diffusion Coefficients via Two-photon Fluorescence Recovery After Photobleaching

Measuring Diffusion Coefficients via Two-photon Fluorescence Recovery After Photobleaching

Published on: February 26, 2010

Optical-Cue-Guided Diffusion Probabilistic Model for Reflection Removal.

Feiyang Zhang, Yuenan Li, Xiaoliang Chang

IEEE Transactions on Neural Networks and Learning Systems

|October 1, 2025

Summary

This summary is machine-generated.

This study introduces a novel reflection removal algorithm using a flash-based optical cue and a diffusion model. The method effectively restores transmission image details, even with strong reflections, outperforming existing techniques.

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Last Updated: Apr 28, 2026

Measuring Diffusion Coefficients via Two-photon Fluorescence Recovery After Photobleaching

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Published on: February 26, 2010

Fluorescence Recovery after Merging a Droplet to Measure the Two-dimensional Diffusion of a Phospholipid Monolayer

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Published on: October 15, 2015

Agarose-based Tissue Mimicking Optical Phantoms for Diffuse Reflectance Spectroscopy

Agarose-based Tissue Mimicking Optical Phantoms for Diffuse Reflectance Spectroscopy

Published on: August 22, 2018

Area of Science:

Computer Vision
Image Processing
Artificial Intelligence

Background:

Reflections in images significantly degrade visual quality and hinder downstream tasks.
Existing reflection removal methods often struggle with complex scenes and strong reflections.

Purpose of the Study:

To develop a novel algorithm for accurate reflection removal from images.
To leverage flash-based optical cues and diffusion models for enhanced image reconstruction.

Main Methods:

Integration of a flash-based optical cue into a diffusion model.
Derivation of a flash-only image to guide transmission image recovery.
Feature distillation for inferring chromatic attributes and modulating generative priors.
Development of a plug-and-play fidelity-enhancing module (FEM).

Main Results:

The proposed algorithm demonstrates superior quantitative and qualitative performance compared to state-of-the-art methods.
Accurate restoration of visual details in the presence of strong reflections.
Satisfactory robustness against nonlinear image representation and misalignment.

Conclusions:

The novel algorithm effectively removes reflections by combining optical cues with diffusion model capabilities.
The method achieves high fidelity reconstruction and reduces artifacts, outperforming existing approaches in real-world scenarios.