Ishiyama, S. and Lu, H. and Soomro, A.A. and Mokhtar, A.A. (2021) A novel single image reflection removal method. In: UNSPECIFIED.
Full text not available from this repository.Abstract
In recent years, reflection is a kind of noise in images which is frequently generated by reflections from windows, glasses and so on when you take pictures or movies. The reflection does not only degrade the image quality, but also affects computer vision tasks such as object detection and segmentation. In SIRR, learning models are often used because various patterns of reflection are possible, and the versatility of the model is required. In this study, we propose a deep learning model for SIRR. There are two problems with the conventional SIRR using deep learning models. The assumed scenes of reflection are vary, and there is little training data because it is difficult to obtain true values. In this study, we focus on the latter and propose an SIRR based on meta-learning. In this study, we adopt MAML, which is one of the methods of meta-learning. In this study, we propose an SIRR using a deep learning model with MAML, which is one of the methods of meta-learning. The deep learning model includes the Iterative Boost Convolutional LSTM Network (IBCLN) is adopted as the deep learning methods. Proposed method improve accuracy compared with conventional method of state-of-the-art result in SIRR. © 2021 SPIE.
Item Type: | Conference or Workshop Item (UNSPECIFIED) |
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Additional Information: | cited By 0; Conference of International Symposium on Artificial Intelligence and Robotics 2021 ; Conference Date: 21 August 2021 Through 27 August 2021; Conference Code:174443 |
Uncontrolled Keywords: | Computer vision; Iterative methods; Long short-term memory; Object detection, Deep learning; Learning models; Metalearning; Reflection removals; Removal method; Single images; SIRR; Small training; Small training data; Training data, Image segmentation |
Depositing User: | Mr Ahmad Suhairi UTP |
Date Deposited: | 10 Nov 2023 03:30 |
Last Modified: | 10 Nov 2023 03:30 |
URI: | https://khub.utp.edu.my/scholars/id/eprint/15622 |