Paper Info
Title
Alternative Underwater Image Restoration Based on Unsupervised Learning and Autoencoder with Degradation Block
Abstract
Underwater image restoration represents a challenge to computer vision and image processing based on machine learning. Recent methodologies to tackle this problem are based on learning. However, the lack of paired image datasets leads the researches to synthesize datasets. We present a new unsupervised learning algorithm that no requires paired dataset to train an encoder-decoder-like neural network for underwater image restoration. Encoder-decoder network learns to represent its input data in a latent representation and reconstruct then in the output. During the training stage, our algorithm applies the output image to a degradation block based on image formation model that reinforces its degradation. The degraded and input images are matched using a loss function. After the training process, we are able to obtain a restored image from the decoder. We focus on underwater inspection and our method relies on small dataset and a light neural network. Underwater images are used to evaluate and validate our algorithm. The qualitative and quantitative results show the improvement provided by our method.
Year
DOI
Venue
2020
10.1109/LARS/SBR/WRE51543.2020.9307136
2020 Latin American Robotics Symposium (LARS), 2020 Brazilian Symposium on Robotics (SBR) and 2020 Workshop on Robotics in Education (WRE)
Keywords
DocType
ISSN
underwater image restoration,degradation block,image processing,machine learning,paired image datasets,unsupervised learning algorithm,encoder-decoder-like neural network,output image,image formation model,degraded input images,latent representation,loss function
Conference
2639-1775
ISBN
Citations 
PageRank 
978-1-6654-1985-7
0
0.34
References 
Authors
0
4
Name
Order
Citations
PageRank
Claudio D. Mello100.34
Bryan U. Moreira200.34
Paulo L. J. Drews-Jr300.34
Silvia Silva Da Costa Botelho45918.57