3D Quasi-Recurrent Neural Network for Hyperspectral Image Denoising (2003.04547v1)

Abstract: In this paper, we propose an alternating directional 3D quasi-recurrent neural network for hyperspectral image (HSI) denoising, which can effectively embed the domain knowledge -- structural spatio-spectral correlation and global correlation along spectrum. Specifically, 3D convolution is utilized to extract structural spatio-spectral correlation in an HSI, while a quasi-recurrent pooling function is employed to capture the global correlation along spectrum. Moreover, alternating directional structure is introduced to eliminate the causal dependency with no additional computation cost. The proposed model is capable of modeling spatio-spectral dependency while preserving the flexibility towards HSIs with arbitrary number of bands. Extensive experiments on HSI denoising demonstrate significant improvement over state-of-the-arts under various noise settings, in terms of both restoration accuracy and computation time. Our code is available at https://github.com/Vandermode/QRNN3D.

• The paper proposes a novel 3D QRNN architecture that combines 3D convolutions and quasi-recurrent pooling to extract intrinsic spatio-spectral features for superior denoising performance.
• It introduces an innovative alternating directional structure to model long-range spectral dependencies while reducing computational overhead.
• Experimental results show significant gains in PSNR and SSIM, establishing the method’s robust performance and applicability across diverse hyperspectral datasets.

Overview of 3D Quasi-Recurrent Neural Network for Hyperspectral Image Denoising

The paper presents a novel approach to hyperspectral image (HSI) denoising utilizing a 3D quasi-recurrent neural network (QRNN3D). This method addresses common noise issues encountered in HSIs, such as Gaussian, stripe, deadline, and impulse noises, by introducing an architecture that effectively integrates structural spatio-spectral correlation and global correlation along the spectrum.

The QRNN3D leverages a three-dimensional convolutional framework to capture local spatio-spectral structures while employing a quasi-recurrent pooling function to model long-range dependencies across spectral bands. An innovative alternating directional structure is proposed to mitigate causal dependencies without incurring additional computational costs, enhancing the model's flexibility to accommodate HSIs with varying spectral bands.

Detailed Methodology

The QRNN3D model is constructed on the principles of deep learning and domain knowledge embedding. It consists of two main components:

1. 3D Convolutional Subcomponent: 3D convolutions are employed to extract intrinsic spatio-spectral features, which surpass traditional 2D methodologies in capturing spectral correlations integral to HSI data. This design ensures that the model can accommodate the complex nature of HSIs and supports its adaptability to varying spectral dimensions.
2. Quasi-Recurrent Pooling: This function dynamically computes pooling weights conditioned on the input features, facilitating an adaptive approach to learn global spectral correlations. It ensures that the inter-band dependencies are effectively exploited, a critical factor that enhances denoising accuracy.

The innovative alternating directional structure proposed in this model diverges from conventional bidirectional structures by alternating the computation direction in each layer. This augmentation considerably reduces computational overhead while achieving comparable performance levels in modeling spectral dependencies.

Experimental Validation

The QRNN3D was subjected to extensive experimentation and compared to both traditional and existing deep learning-based denoising methods. It demonstrated significant improvements in denoising effectiveness across various noise scenarios, including complex noise conditions that integrate multiple noise types. Key findings from the experiments include:

• Superior denoising capability in terms of PSNR and SSIM metrics compared to state-of-the-art approaches, indicating substantial gains in image quality post-processing.
• Remarkable model flexibility that allows application over HSIs of different spectral resolutions without retraining, highlighting its potential in real-world applications involving diverse sensor technologies.
• Robust computational efficiency, characterized by reduced processing time compared to prior methods, underscoring the practical viability of the model in resource-constrained environments.

Implications and Future Directions

The development of the 3D QRNN3D for HSI denoising marks a meaningful enhancement over existing methods, providing a balanced approach to handling the spectral and spatial diversities inherent in HSIs. The proposed model not only offers substantial improvements in terms of denoising quality but also sets a precedent for future exploration into spectrally-invariant neural networks.

Potential directions for future research include further investigation into network architectures capable of improved spectral transferability across different HSI datasets, as well as the integration of additional spectral and spatial priors to enhance denoising performance. Additionally, extending this methodology to other spectral imaging domains, such as medical imaging or environmental monitoring, could validate its utility in broader applications. Through these advances, the QRNN3D can significantly contribute to the refinement of HSI analysis techniques, offering more accurate and efficient data processing solutions.