DSRD: deep sparse representation with learnable dictionary for remotely sensed image denoising

Patch-based modelling methods (i.e. sparse coding) had proven their great ability in solving the degradation problem caused by additive white Gaussian noise (AWGN) in remotely sensing images. However, these methods usually pursue surprising performance with sacrificing computational efficiency, and the learned dictionary is universal, resulting in a weak model representation flexibility. To address these issues, this paper proposes a deep sparse representation with learnable dictionary (DSRD) scheme, where the major difference from the previous sparse coding methods is that sparse representation coefficients and dictionaries are both deeply learned, acted as two modular parts to be plugged into the unfolded sparse coding model, to speed up for a stable solution as well as yielding competitive performance. The ablation studies illustrate that our DSRD strategy is efficient for a fixed solution while a flexible and powerful capacity in producing pleasing denoising performance. Meanwhile, the comparisons of quantitatively and qualitatively experimental results further demonstrate that it is effective and can generate enjoyable denoising performance that even outperforms that produced by other state-of-the-arts.