# Y-net: Multi-scale feature aggregation network with wavelet structure similarity loss function for single image dehazing

arXiv: https://arxiv.org/abs/2003.13912 · HTML: https://mlsanity.com/paper/2003.13912/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Mar 2020.

## Built on by (most cited first, 14 of 14)

- [NTIRE 2021 Depth Guided Image Relighting Challenge](https://mlsanity.com/paper/2104.13365/) (2021)
- [Robust Representation Learning via Perceptual Similarity Metrics](https://mlsanity.com/paper/2106.06620/) (2021)
- [A Comprehensive Survey and Taxonomy on Single Image Dehazing Based on Deep Learning](https://mlsanity.com/paper/2106.03323/) (2021)
- [NTIRE 2022 Challenge on Stereo Image Super-Resolution: Methods and Results](https://mlsanity.com/paper/2204.09197/) (2022)
- [S3Net: A Single Stream Structure for Depth Guided Image Relighting](https://mlsanity.com/paper/2105.00681/) (2021)
- [LAFFNet: A Lightweight Adaptive Feature Fusion Network for Underwater Image Enhancement](https://mlsanity.com/paper/2105.01299/) (2021)
- [Multi-modal Bifurcated Network for Depth Guided Image Relighting](https://mlsanity.com/paper/2105.00690/) (2021)
- [WaveletFormerNet: A Transformer-based Wavelet Network for Real-world Non-homogeneous and Dense Fog Removal](https://mlsanity.com/paper/2401.04550/) (2024)
- [WaveNet-SF: A Hybrid Network for Retinal Disease Detection Based on Wavelet Transform in Spatial-Frequency Domain](https://mlsanity.com/paper/2501.11854/) (2025)
- [F2former: When Fractional Fourier Meets Deep Wiener Deconvolution and Selective Frequency Transformer for Image Deblurring](https://mlsanity.com/paper/2409.02056/) (2024)
- [Robust Unsupervised Multi-Object Tracking in Noisy Environments](https://mlsanity.com/paper/2105.10005/) (2021)
- [Haze-Aware Attention Network for Single-Image Dehazing](https://mlsanity.com/paper/2407.11505/) (2024)
- [Complex Wavelet Mutual Information Loss: A Multi-Scale Loss Function for Semantic Segmentation](https://mlsanity.com/paper/2502.00563/) (2025)
- [DedustNet: A Frequency-dominated Swin Transformer-based Wavelet Network for Agricultural Dust Removal](https://mlsanity.com/paper/2401.04750/) (2024)

## Abstract (arXiv)

Single image dehazing is the ill-posed two-dimensional signal reconstruction problem. Recently, deep convolutional neural networks (CNN) have been successfully used in many computer vision problems. In this paper, we propose a Y-net that is named for its structure. This network reconstructs clear images by aggregating multi-scale features maps. Additionally, we propose a Wavelet Structure SIMilarity (W-SSIM) loss function in the training step. In the proposed loss function, discrete wavelet transforms are applied repeatedly to divide the image into differently sized patches with different frequencies and scales. The proposed loss function is the accumulation of SSIM loss of various patches with respective ratios. Extensive experimental results demonstrate that the proposed Y-net with the W-SSIM loss function restores high-quality clear images and outperforms state-of-the-art algorithms. Code and models are available at https://github.com/dectrfov/Y-net.