Advances in the design of multi-spectral cameras have

led to great interests in a wide range of applications, from

astronomy to autonomous driving. However, such cameras

inherently suffer from a trade-off between the spatial and

spectral resolution. In this paper, we propose to address

this limitation by introducing a novel method to carry out

super-resolution on raw mosaic images, multi-spectral or

RGB Bayer, captured by modern real-time single-shot mo-

saic sensors. To this end, we design a deep super-resolution

architecture that benefits from a sequential feature pyramid

along the depth of the network. This, in fact, is achieved

by utilizing a convolutional LSTM (ConvLSTM) to learn the

inter-dependencies between features at different receptive

fields. Additionally, by investigating the effect of different

attention mechanisms in our framework, we show that a

ConvLSTM inspired module is able to provide superior at-

tention in our context. Our extensive experiments and anal-

yses evidence that our approach yields significant super-

resolution quality, outperforming current state-of-the-art

mosaic super-resolution methods on both Bayer and multi-

spectral images. Additionally, to the best of our knowledge,

our method is the first specialized method to super-resolve

mosaic images, whether it be multi-spectral or Bayer.