Uncooled Thermal Image Denoising using Deep Convolutional Neural Network

Abstract

Thermal imaging which initially originated for military applications owing to the fact that it can produce a clear image on darkest nights as they need no light to operate thus allow seeing without being seen. Thermal imaging cameras can also see to some extent through snow, rain, fog and therefore find its application in thermal weapon sight, night vision for tanks and surveillance. However images captured are contaminated by noise during image acquisition, compression and transmission which can severely hamper successful image analysis and tracking. In this work we used a denoising convolutional neural network to reduce Gaussian noise from the images acquired through uncooled thermal imagers. From the acquired images, 100 images were segmented into patches to train the network which resulted into improved image quality metrics which are indicated through experimental results resulting into higher peak signal-to-noise ratio.