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

ArXi:2301.06081v4 Announce Type: replace-cross 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.