# The Effectiveness of Instance Normalization: a Strong Baseline for Single Image Dehazing

arXiv: https://arxiv.org/abs/1805.03305 · HTML: https://mlsanity.com/paper/1805.03305/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 May 2018.

## Built on by (most cited first, 4 of 4)

- [A Survey on the Robustness of Computer Vision Models against Common Corruptions](https://mlsanity.com/paper/2305.06024/) (2023)
- [A GAN-Based Input-Size Flexibility Model for Single Image Dehazing](https://mlsanity.com/paper/2102.09796/) (2021)
- [End-to-end Keyword Spotting using Xception-1d](https://mlsanity.com/paper/2110.07498/) (2021)
- [Visible to Thermal image Translation for improving visual task in low light conditions](https://mlsanity.com/paper/2310.20190/) (2023)

## Abstract (arXiv)

We propose a novel deep neural network architecture for the challenging problem of single image dehazing, which aims to recover the clear image from a degraded hazy image. Instead of relying on hand-crafted image priors or explicitly estimating the components of the widely used atmospheric scattering model, our end-to-end system directly generates the clear image from an input hazy image. The proposed network has an encoder-decoder architecture with skip connections and instance normalization. We adopt the convolutional layers of the pre-trained VGG network as encoder to exploit the representation power of deep features, and demonstrate the effectiveness of instance normalization for image dehazing. Our simple yet effective network outperforms the state-of-the-art methods by a large margin on the benchmark datasets.