  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.
