Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Adaptations that Reduce Water Loss

Adaptations that Reduce Water Loss

Though evaporation from plant leaves drives transpiration, it also results in loss of water. Because water is critical for photosynthetic reactions and other cellular processes, evolutionary pressures on plants in different environments have driven the acquisition of adaptations that reduce water loss.

Responses to Drought and Flooding

Responses to Drought and Flooding

Water plays a significant role in the life cycle of plants. However, insufficient or excess of water can be detrimental and pose a serious threat to plants.

Design Example: Design of an Irrigation Channel

Design Example: Design of an Irrigation Channel

Trapezoidal channels are widely used in irrigation systems due to their cost-effectiveness and efficiency in conveying water. Trapezoidal channels feature a flat bottom and sloping sides, making them stable and easier to construct compared to other shapes. The bottom width and side slope ratio are determined based on the required flow capacity and site conditions. The side slope is kept gentle for unlined channels to prevent soil erosion.Hydraulic parameters in channel design include the flow...

Precipitation Processes

Precipitation Processes

The experimental conditions in a gravimetric analysis should be optimized to maximize the particle size and purity of the obtained precipitate. Ideally, the concentration of the precipitating reagent should be low with effective stirring to maintain low relative supersaturation for the growth of large crystals. In homogeneous precipitation, the precipitant is slowly generated by a chemical reaction in the solution to avoid local reagent excesses. For example, urea decomposes gradually to...

Precipitation and Co-precipitation

Precipitation and Co-precipitation

Precipitation and coprecipitation methods can be used to separate a mixture of ions in a solution. In qualitative inorganic analysis, ions that form sparingly soluble precipitates with the same reagent are separated based on the differences in solubility products. For example, consider the separation of Cu(II) and Fe(II) ions by precipitation as insoluble sulfides. First, copper(II) sulfide is precipitated by the addition of acidic H2S, where the dissociation of H2S is suppressed. Adding H2S...

Preserving Water Sources

Preserving Water Sources

Preserving Water SourcesWater is one of the most important resources on Earth. We use it daily to drink, cook, clean, grow food, and have fun. But clean water isn’t unlimited. Only a small amount of the water on Earth is fresh and safe to use, so it’s super important to take care of it. Preserving water resources means protecting rivers, lakes, and groundwater from pollution and not wasting water. Learning to use water wisely helps people understand how to care for their environment and...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Image Sand-Dust Removal Using Reinforced Multiscale Image Pair Training.

Sensors (Basel, Switzerland)·2025

Same author

Tone Image Classification and Weighted Learning for Visible and NIR Image Fusion.

Entropy (Basel, Switzerland)·2023

Same author

Combined Deep Learning Techniques for Mandibular Fracture Diagnosis Assistance.

Life (Basel, Switzerland)·2022

Same author

Automatic Detection of Mandibular Fractures in Panoramic Radiographs Using Deep Learning.

Diagnostics (Basel, Switzerland)·2021

Search research articles

Related Experiment Video

Updated: Aug 13, 2025

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

Published on: October 27, 2023

Rainwater-Removal Image Conversion Learning with Training Pair Augmentation.

Yu-Keun Han¹, Sung-Woon Jung¹, Hyuk-Ju Kwon¹

¹School of Electronic and Electrical Engineering, Kyungpook National University, 80 Deahakro, Buk-Gu, Daegu 702-701, Republic of Korea.

Entropy (Basel, Switzerland)

|January 21, 2023

Summary

This study introduces a deep learning method to remove raindrops from images. It uses a Pix2pix generative adversarial network (GAN) and generates virtual data to improve learning efficiency for raindrop removal.

Keywords:

GAN Pix2pix augmentation learning image-to-image learning rainwater removal

More Related Videos

DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning

DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning

Published on: May 10, 2024

Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation

Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation

Published on: February 9, 2024

Related Experiment Videos

Last Updated: Aug 13, 2025

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

Published on: October 27, 2023

DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning

DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning

Published on: May 10, 2024

Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation

Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation

Published on: February 9, 2024

Area of Science:

Computer Vision
Artificial Intelligence
Image Processing

Background:

Removing raindrops from images is challenging due to data requirements and noise in traditional methods.
Existing deep learning approaches often need extensive datasets of real-world images.

Purpose of the Study:

To develop an efficient deep learning method for removing raindrops from camera lens images.
To address the data acquisition challenges in training image restoration models.

Main Methods:

Utilized the Pix2pix generative adversarial network (GAN) for image-to-image translation.
Proposed a novel approach for generating virtual raindrop image data.
Employed a convolutional neural network (CNN) for effective identification of generated data.

Main Results:

Successfully demonstrated an efficient image conversion method for raindrop removal.
The Pix2pix GAN model effectively learned and transformed images.
Virtual data generation significantly improved the learning process and data acquisition.

Conclusions:

The proposed method offers an effective solution for removing raindrops from images using deep learning.
Generating virtual data is a viable strategy to overcome limitations in real-world data collection.
This technique enhances the robustness and efficiency of image restoration models.