Partial-DNet: A Novel Blind Denoising Model with Noise Intensity Estimation for HSI

Because of the inevitable noise interference in hyperspectral images (HSIs), the understanding and application of HSIs are seriously restricted. To solve this problem, the research on the data-driven neural network-based denoising method has become a hotspot in recent years. However, for HSIs with inconsistent and mixed noises, the traditional data-driven denoising algorithms have obvious limitations on generalization ability. In order to overcome these drawbacks, a novel partial densenet (Partial-DNet) model with noise intensity estimation is proposed for HSIs blind denoising in this article. In the proposed Partial-DNet model, the noise intensity of each band is estimated in the first and then fused with the observed images to generate the feature maps by introducing the channel attention mechanism. Finally, a novel multiscale neural network is explored to extract the spatial-spectral joint features. The contributions of this article can be summarized as follows: 1) the noise intensity of each band is estimated as a prior to guide the blind denoising framework suit with different data sets adaptively; 2) a Partial-DNet model is proposed to extract multiscale spatial-spectral features more efficient to maintain the details better while denoising; and 3) the experiments on both simulated and real HSI data sets indicate that the proposed denoising framework can be used to remove the inconsistent mixed noises in HSIs adaptively. Compared with the other state-of-the-art denoising methods, HSIs denoised by the proposed Partial-DNet algorithm not only have a higher PSNR index but also have higher classification accuracy under the same situation.