  Single-Image-Super-Resolution (SISR) is a classical computer vision problem
that has benefited from the recent advancements in deep learning methods,
especially the advancements of convolutional neural networks (CNN). Although
state-of-the-art methods improve the performance of SISR on several datasets,
direct application of these networks for practical use is still an issue due to
heavy computational load. For this purpose, recently, researchers have focused
on more efficient and high-performing network structures. Information
multi-distilling network (IMDN) is one of the highly efficient SISR networks
with high performance and low computational load. IMDN achieves this efficiency
with various mechanisms such as Intermediate Information Collection (IIC),
working in a global setting, Progressive Refinement Module (PRM), and Contrast
Aware Channel Attention (CCA), employed in a local setting. These mechanisms,
however, do not equally contribute to the efficiency and performance of IMDN.
In this work, we propose the Global Progressive Refinement Module (GPRM) as a
less parameter-demanding alternative to the IIC module for feature aggregation.
To further decrease the number of parameters and floating point operations
persecond (FLOPS), we also propose Grouped Information Distilling Blocks
(GIDB). Using the proposed structures, we design an efficient SISR network
called IMDeception. Experiments reveal that the proposed network performs on
par with state-of-the-art models despite having a limited number of parameters
and FLOPS. Furthermore, using grouped convolutions as a building block of GIDB
increases room for further optimization during deployment. To show its
potential, the proposed model was deployed on NVIDIA Jetson Xavier AGX and it
has been shown that it can run in real-time on this edge device
