Huanhua Liu, Wei Wang*, Hanyu Liu, Shuheng Yi, Yonghao Yu, Xunwen Yao
CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.1, pp. 459-472, 2024, DOI:10.32604/cmes.2023.029084
Abstract Deep Convolutional Neural Networks (CNNs) have achieved high accuracy in image classification tasks, however, most existing models are trained on high-quality images that are not subject to image degradation. In practice, images are often affected by various types of degradation which can significantly impact the performance of CNNs. In this work, we investigate the influence of image degradation on three typical image classification CNNs and propose a Degradation Type Adaptive Image Classification Model (DTA-ICM) to improve the existing CNNs’ classification accuracy on degraded images. The proposed DTA-ICM comprises two key components: a Degradation Type Predictor (DTP) and a Degradation Type… More >
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