  In the hyperspectral image (HSI) denoising task, the real noise embedded in
the HSI is always complex and diverse so that many model-based HSI denoising
methods only perform well on some specific noisy HSIs. To enhance the noise
adaptation capability of current methods, we first resort to the weighted HSI
denoising model since its weight is capable of characterizing the noise in
different positions of the image. However, the weight in these weighted models
is always determined by an empirical updating formula, which does not fully
utilize the noise information contained in noisy images and thus limits their
performance improvement. In this work, we propose an automatic weighting scheme
to alleviate this issue. Specifically, the weight in the weighted model is
predicted by a hyper-weight network (i.e., HWnet), which can be learned in a
bi-level optimization framework based on the data-driven methodology. The
learned HWnet can be explicitly plugged into other weighted denoising models,
and help adjust weights for different noisy HSIs and different weighted models.
Extensive experiments verify that the proposed HWnet can help improve the
generalization ability of a weighted model to adapt to more complex noise, and
can also strengthen the weighted model by transferring the knowledge from
another weighted model. Additionally, to explain the experimental results, we
also theoretically prove the training error and generalization error upper
bound of the proposed HWnet, which should be the first generalization error
analysis in the low-level vision field as far as we know.
