An Optimization-Driven Network With Knowledge Prior Injection for HSI Denoising

Due to the limitations of sensor hardware devices, the hyperspectral image (HSI) often suffers from various types of noise, such as Gaussian noise, impulse noise, stripe noise, and deadlines, which can significantly degrade their quality. Although many data-driven methods have been proposed to deal with complex noise, few of them consider the structural characteristics of noise. This not only leads to a lack of interpretability but also results in poor performance when dealing with structural noise in practical applications. To address this issue, this article proposes KPInet, a convolutional neural network (CNN) driven by the structural knowledge of noise for HSI denoising. First and foremost, the knowledge optimization-driven module (KODM) utilizes the deep unrolling method to unfold a total variation (TV) algorithm that considers the structural characteristics of noise. This approach improves the network’s interpretability and results in better performance on structural noise, while maintaining the effect of removing Gaussian noise. Second, the statistical feature injection module (SFIM) extracts more features by utilizing spectral gradients, medians, and means of the HSI. Third, the multiscale degradation guidance module (MDGM) utilizes a dual-stream decoder with a low-resolution upsampling guidance branch to better distinguish the real structure and noise structure in the HSI. Experimental results on simulated and real datasets indicate that the approach achieves favorable denoising performance, as evidenced by both quantitative evaluation metrics and visual results. Furthermore, it also demonstrates the robustness and generalization capacity of the proposed KPInet.