RHDDNet: multi-label classification-based detection of image hybrid distortions

Bowen Dou; Hai Li; Shujuan Hou

doi:10.1117/12.2643514

12 October 2022 RHDDNet: multi-label classification-based detection of image hybrid distortions

Bowen Dou, Hai Li, Shujuan Hou

Proceedings Volume 12342, Fourteenth International Conference on Digital Image Processing (ICDIP 2022); 123421S (2022) https://doi.org/10.1117/12.2643514
Event: Fourteenth International Conference on Digital Image Processing (ICDIP 2022), 2022, Wuhan, China

Abstract

Image distortion detection is a key step in image quality assessment and image reconstruction algorithms. In previous work, a large number of research focus on detecting the single distortion in the image. However, the number of distortion types in the image is often uncertain. Thus, we propose a model that can be used for hybrid distortion detection. Concretely, we transform the hybrid distortion detection task into a multi-label classification task and abstract it as a convolutional network optimization problem. A dataset is created to train the model and evaluate its performance. Experiments show that the proposed model performs well in the detection of hybrid distortions in images.

Citation Download Citation

Bowen Dou, Hai Li, and Shujuan Hou "RHDDNet: multi-label classification-based detection of image hybrid distortions", Proc. SPIE 12342, Fourteenth International Conference on Digital Image Processing (ICDIP 2022), 123421S (12 October 2022); https://doi.org/10.1117/12.2643514

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