Handling Data Imbalance for Improving Blurriness Estimation using Convolutional Transformer

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 e...

Full description

Saved in:
Bibliographic Details
Published in2023 International Technical Conference on Circuits/Systems, Computers, and Communications (ITC-CSCC) pp. 1 - 6
Main Authors Lee, HyunYong, Kim, Nac-Woo, Lee, Jungi, Ko, Seok-Kap
Format Conference Proceeding
LanguageEnglish
Published IEEE 25.06.2023
Subjects
Blurriness estimation
Computational modeling
Computer vision
convolutional transformer
data imbalance
Data models
Estimation
Feature extraction
Image restoration
oversampling
Transformers
weighted loss
Online AccessGet full text

Cover

More Information
Summary: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.
DOI:10.1109/ITC-CSCC58803.2023.10212798