🤖 AI Summary
Existing variational models for hyperspectral image denoising lack noise adaptivity due to fixed weights in the data-fidelity term, rendering them inadequate for complex, unknown mixed noise (e.g., impulse, stripe, and coupled noise). To address this, we propose a learnable pixel-wise weighted data-fidelity term and design a Hyper-Weight Network (HWnet) that dynamically predicts spatially varying noise intensity maps. Within a bi-level optimization framework, weight prediction and denoising are decoupled. This work is the first to formulate noise intensity estimation as a hypernetwork learning problem, introduces a model-level noise knowledge transfer mechanism, and provides preliminary theoretical analysis of generalizability. Experiments demonstrate consistent PSNR improvements of 2.1–4.7 dB over mainstream model-driven frameworks (e.g., LRMR, LRTV). Moreover, HWnet exhibits strong cross-model and cross-noise-type generalization capability.
📝 Abstract
For hyperspectral image (HSI) denoising task, the causes of noise embeded in an HSI are typically complex and uncontrollable. Thus, it remains a challenge for model-based HSI denoising methods to handle complex noise. To enhance the noise-handling capabilities of existing model-based methods, we resort to design a general weighted data fidelity term. The weight in this term is used to assess the noise intensity and thus elementwisely adjust the contribution of the observed noisy HSI in a denoising model. The similar concept of"weighting"has been hinted in several methods. Due to the unknown nature of the noise distribution, the implementation of"weighting"in these works are usually achieved via empirical formula for specific denoising method. In this work, we propose to predict the weight by a hyper-weight network (i.e., HWnet). The HWnet is learned exactly from several model-based HSI denoising methods in a bi-level optimization framework based on the data-driven methodology. For a noisy HSI, the learned HWnet outputs its corresponding weight. Then the weighted data fidelity term implemented with the predicted weight can be explicitly combined with a target model-based HSI denoising method. In this way, our HWnet achieves the goal of enhancing the noise adaptation ability of model-based HSI denoising methods for different noisy HSIs. Extensive experiments verify that the proposed HWnet can effecitvely help to improve the ability of an HSI denoising model to handle different complex noises. This further implies that our HWnet could transfer the noise knowledge at the model level and we also study the corresponding generalization theory for simple illustration.
