# Inception-Residual Block based Neural Network for Thermal Image Denoising

arXiv: https://arxiv.org/abs/1810.13169 · HTML: https://mlsanity.com/paper/1810.13169/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Oct 2018.

## Built on by (most cited first, 1 of 1)

- [NAADA: A Noise-Aware Attention Denoising Autoencoder for Dental Panoramic Radiographs](https://mlsanity.com/paper/2506.19387/) (2025)

## Abstract (arXiv)

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.