Fidelity Estimation Improves Noisy-Image Classification With Pretrained Networks

• Published in: IEEE signal processing letters Vol. 28; pp. 1719 - 1723
• Main Authors: Lin, Xiaoyu, Bhattacharjee, Deblina, El Helou, Majed, Susstrunk, Sabine
• Format: Journal Article
• Language: English
• Published: New York IEEE 2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Accuracy; Artificial neural networks; Classification; Data Fidelity; Data mining; Deep learning; Degradation; Feature extraction; Image classification; Image restoration; Machine learning; Noise level; Noise levels; Noise measurement; Noisy-Image Classification; Robustness
• ISSN: 1070-9908; 1558-2361
• DOI: 10.1109/LSP.2021.3104769

Image classification has significantly improved using deep learning. This is mainly due to convolutional neural networks (CNNs) that are capable of learning rich feature extractors from large datasets. However, most deep learning classification methods are trained on clean images and are not robust when handling noisy ones, even if a restoration preprocessing step is applied. While novel methods address this problem, they rely on modified feature extractors and thus necessitate retraining. We instead propose a method that can be applied on a pretrained classifier. Our method exploits a fidelity map estimate that is fused into the internal representations of the feature extractor, thereby guiding the attention of the network and making it more robust to noisy data. We improve the noisy-image classification (NIC) results by significantly large margins, especially at high noise levels, and come close to the fully retrained approaches. Furthermore, as proof of concept, we show that when using our oracle fidelity map we even outperform the fully retrained methods, whether trained on noisy or restored images.