Shadow Removal from Images Using Conditional GANs

Authors

  • Amrit Acharya Department of Electronics and Computer Engineering, IOE, Pashchimanchal Campus, Tribhuvan University, Nepal
  • Ramesh Thapa Department of Electronics and Computer Engineering, IOE, Pashchimanchal Campus, Tribhuvan University, Nepal

DOI:

https://doi.org/10.3126/jes2.v2i1.60379

Keywords:

CGAN, GAN, Shadow detection, shadow removal, U-net

Abstract

Shadow removal has many applications in computer vision and shadow-free images have better visual quality. In recent studies, deep learning-based CNN models have shown better performance than traditional approaches to shadow removal. GAN takes the advantage of two independent neural networks. This study about shadow removal is implemented using GAN. Shadow removal is divided into two tasks: detection and removal. The two sub-networks stacked upon each other are based on conditional GAN. The input shadow image 256*256 is fed to the first generator network to produce a shadow mask, which is input to the second generator network along with a shadow image to obtain a shadow-free image.

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Published

2023-12-06

How to Cite

Acharya, A., & Thapa, R. (2023). Shadow Removal from Images Using Conditional GANs. Journal of Engineering and Sciences, 2(1), 19–23. https://doi.org/10.3126/jes2.v2i1.60379

Issue

Vol. 2 No. 1 (2023)

Section

Articles

License

Copyright (c) 2023 Amrit Acharya, Ramesh Thapa

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

CC BY: This license allows reusers to distribute, remix, adapt, and build upon the material in any medium or format, so long as attribution is given to the creator. The license allows for commercial use.