Abstract
Blur is a common phenomenon in image acquisition that negatively influences the recognition rate of most classifiers. This paper studies the influence of image blurring of various types and sizes on the recognition rate achieved by a deep convolutional network. We confirm that the blur significantly decreases the performance if the network has been trained on clear images only. When the training set is augmented with blurred samples, the recognition rate becomes sufficiently high even if the blur in query images is of different size than the blur used for training. However, this is mostly not true if query images contain blur of a different type from the one used for training.
This work was supported by the Czech Science Foundation (Grant No. GA21-03921S) and by the Praemium Academiae.
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Lébl, M., Šroubek, F., Flusser, J. (2023). Impact of Image Blur on Classification and Augmentation of Deep Convolutional Networks. In: Gade, R., Felsberg, M., Kämäräinen, JK. (eds) Image Analysis. SCIA 2023. Lecture Notes in Computer Science, vol 13886. Springer, Cham. https://doi.org/10.1007/978-3-031-31438-4_8
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DOI: https://doi.org/10.1007/978-3-031-31438-4_8
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