  3D convolutions are commonly employed by demosaicking neural models, in the
same way as solving other image restoration problems. Counter-intuitively, we
show that 3D convolutions implicitly impede the RGB color spectra from
exchanging complementary information, resulting in spectral-inconsistent
inference of the local spatial high frequency components. As a consequence,
shallow 3D convolution networks suffer the Moir\'e artifacts, but deep 3D
convolutions cause over-smoothness. We analyze the fundamental difference
between demosaicking and other problems that predict lost pixels between
available ones (e.g., super-resolution reconstruction), and present the
underlying reasons for the confliction between Moir\'e-free and
detail-preserving. From the new perspective, our work decouples the common
standard convolution procedure to spectral and spatial feature aggregations,
which allow strengthening global communication in the spectral dimension while
respecting local contrast in the spatial dimension. We apply our demosaicking
model to two tasks: Joint Demosaicking-Denoising and Independently
Demosaicking. In both applications, our model substantially alleviates
artifacts such as Moir\'e and over-smoothness at similar or lower computational
cost to currently top-performing models, as validated by diverse evaluations.
Source code will be released along with paper publication.
