Hyperspectral image denoising via self-modulating convolutional neural networks

作     者：Torun, Orhan Yuksel, Seniha Esen Erdem, Erkut Imamoglu, Nevrez Erdem, Aykut 

作者机构：Hacettepe Univ Inst Sci TR-06800 Ankara Turkiye Hacettepe Univ Dept Elect & Elect Engn TR-06800 Ankara Turkiye Hacettepe Univ Dept Comp Engn TR-06800 Ankara Turkiye Natl Inst Adv Ind Sci & Technol Digital Architecture Res Ctr Tokyo 1350064 Japan Koc Univ Dept Comp Engn TR-34450 Istanbul Turkiye Koc Univ Is Bank AI Ctr TR-34450 Istanbul Turkiye 

出 版 物：《SIGNAL PROCESSING》 (信号处理)

年 卷 期：2024年第214卷

核心收录：

学科分类：0808[工学-电气工程] 08[工学] 

基　　金：AI Research Center Science Academy 

主　　题：HSIs Denoising Spectral self-modulation SM-CNN 

摘      要：Compared to natural images, hyperspectral images (HSIs) consist of a large number of bands, with each band capturing different spectral information from a certain wavelength, even some beyond the visible spectrum. These characteristics of HSIs make them highly effective for remote sensing applications. That said, the existing hyperspectral imaging devices introduce severe degradation in HSIs. Hence, hyperspectral image denoising has attracted lots of attention by the community lately. While recent deep HSI denoising methods have provided effective solutions, their performance under real-life complex noise remains suboptimal, as they lack adaptability to new data. To overcome these limitations, in our work, we introduce a self-modulating convolutional neural network which we refer to as SM-CNN, which utilizes correlated spectral and spatial information. At the core of the model lies a novel block, which we call spectral self-modulating residual block (SSMRB), that allows the network to transform the features in an adaptive manner based on the adjacent spectral data, enhancing the network s ability to handle complex noise. In particular, the introduction of SSMRB transforms our denoising network into a dynamic network that adapts its predicted features while denoising every input HSI with respect to its spatio-spectral characteristics. Experimental analysis on both synthetic and real data shows that the proposed SM-CNN outperforms other state-of-the-art HSI denoising methods both quantitatively and qualitatively on public benchmark datasets. Our code will be available at https://***/orhan-t/SM-CNN.