Multi-stage Enhanced Denoising Network on Hyperspectral Image

• Published in: 2022 3rd International Conference on Information Science, Parallel and Distributed Systems (ISPDS) pp. 141 - 144
• Main Authors: Pan, Xiaomiao, Pan, Qiang, Wang, Chao, Yang, Chuansheng, Yang, Yueting, He, Liangtian
• Format: Conference Proceeding
• Language: English
• Published: IEEE 22.07.2022
• Subjects: Additives; convolutional neural network (CNN); deep learning (DL); hyperspectral image (HSI) denoise; Image restoration; Information science; Maintenance engineering; Neural networks; Noise reduction; Visualization
• DOI: 10.1109/ISPDS56360.2022.9874188

Hyperspectral images (HSIs) will experience noise throughout the data collection process due to the imaging system's limitations, which will make it challenging to extract the image's crucial information. In this paper, a multi-stage enhanced HSI denoising network (MED-Net) is proposed. Our core concept is to process the hyperspectral noise image iteratively using a multi-stage network. A similar network structure's first and second phases are employed for the denoise process. To achieve cross-stage information transfer, we use CSFF (Cross-stage Feature Fusion) mechanism and SAM (Supervised Attention Module). AN (Additive Network) and MN (Multiplicative Network) are used to remove additive and multiplicative noise. Then, we restore the background based on the residual network and attention mechanism. The results of our experiments demonstrate the superiority of our approach over the actual HSIs data recovery, and the restored image has good visual clarity and detail.