Hyperspectral Image Denoising via Self-Modulating Convolutional Neural Networks

TL;DR

This paper introduces SM-CNN, a self-modulating neural network for hyperspectral image denoising that adaptively leverages spectral and spatial information to effectively handle complex noise, outperforming existing methods.

Contribution

The paper proposes a novel spectral self-modulating residual block (SSMRB) that enables adaptive feature transformation, making the denoising network dynamic and more effective for complex noise in HSIs.

Findings

SM-CNN outperforms state-of-the-art methods on benchmark datasets.

The adaptive SSMRB enhances denoising performance under real-world noise.

Experimental results show significant qualitative and quantitative improvements.

Abstract

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.