  Thermal cameras show noisy images due to their limited thermal resolution,
especially for the scenes of a low temperature difference. In order to deal
with a noise problem, this paper proposes a novel neural network architecture
with repeatable denoising inception-residual blocks(DnIRB) for noise learning.
Each DnIRB has two sub-blocks with difference receptive fields and one shortcut
connection to prevent a vanishing gradient problem. The proposed approach is
tested for thermal images. The experimental results indicate that the proposed
approach shows the best SQNR performance and reasonable processing time
compared with state-of-the-art denoising methods.
