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Conference Paper Handling Data Imbalance for Improving Blurriness Estimation using Convolutional Transformer
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Authors
HyunYong Lee, Nac-Woo Kim, Jungi Lee, Seok-Kap Ko
Issue Date
2023-06
Citation
International Technical Conference on Circuits/Systems, Computers and Communications (ITC-CSCC) 2023, pp.1019-1024
Publisher
IEEE
Language
English
Type
Conference Paper
DOI
https://dx.doi.org/10.1109/ITC-CSCC58803.2023.10212798
Abstract
Image deblurring is important pre-processing for various computer vision tasks. In this paper, as one approach for improving image deblurring, we are interested in blurriness estimation. For this, we first propose a model for blurriness estimation. Adopting the convolutional Transformer, we try to extract meaningful features from a blurry image to be used for blurriness estimation. Then, using the proposed model, we examine the usefulness of the known techniques for handling data imbalance issue, that is widely observed in real-world scenarios. Through the experiments using the RealBlur dataset, we show that the weighted loss is not effective in solving the data imbalance issue. On the contrary, the oversampling technique is useful, particularly in improving the estimation performance of the rare data while slightly sacrificing the estimation performance of the prevalent data.
KSP Keywords
Computer Vision(CV), Data imbalance, Image deblurring, Oversampling technique, Pre-processing, Proposed model, Real-world, estimation performance