A Data-driven Loss Weighting Scheme across Heterogeneous Tasks for Image Denoising

Understand

In a variational denoising model, weight in the data fidelity term plays the role of enhancing the noise-removal capability.

• It is profoundly correlated with noise information, while also balancing the data fidelity and regularization terms.
• However, the difficulty of assigning weight is expected to be substantial when the noise pattern is beyond independent identical Gaussian distribution, e.g., impulse noise, stripe noise, or a mixture of several patterns, etc.
• Furthermore, how to leverage weight to balance the data fidelity and regularization terms is even less evident.