  Image denoising is still a challenging issue in many computer vision
sub-domains. Recent studies show that significant improvements are made
possible in a supervised setting. However, few challenges, such as spatial
fidelity and cartoon-like smoothing remain unresolved or decisively overlooked.
Our study proposes a simple yet efficient architecture for the denoising
problem that addresses the aforementioned issues. The proposed architecture
revisits the concept of modular concatenation instead of long and deeper
cascaded connections, to recover a cleaner approximation of the given image. We
find that different modules can capture versatile representations, and
concatenated representation creates a richer subspace for low-level image
restoration. The proposed architecture's number of parameters remains smaller
than the number for most of the previous networks and still achieves
significant improvements over the current state-of-the-art networks.
