Flatness loss for image dehazing

Chenyu Zhang; Qihong Ye; Hongming Chen; Xiaoshuang Wang

doi:10.1117/12.3011504

7 December 2023 Flatness loss for image dehazing

Chenyu Zhang, Qihong Ye, Hongming Chen, Xiaoshuang Wang

Proceedings Volume 12941, International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2023); 1294143 (2023) https://doi.org/10.1117/12.3011504
Event: Third International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 203), 2023, Yinchuan, China

Abstract

In recent years, significant progress has been made in image dehazing, but most dehazing convolutional neural networks only learn from hazy images to the corresponding feature maps of clean images, ignoring the details of the images. In this paper, a new flatness loss function is proposed for single image dehazing, thereby improving the overall effect of dehazing results. This loss function allows the model to focus on the texture features of hazy images and clean images and supervises edge information by reducing the flatness difference between clean pixels and blurred pixels. The experiments on the benchmark dataset using the flatness loss function on the single image dehazing model show that this method can effectively improve the quantitative performance of the model.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Chenyu Zhang, Qihong Ye, Hongming Chen, and Xiaoshuang Wang "Flatness loss for image dehazing", Proc. SPIE 12941, International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2023), 1294143 (7 December 2023); https://doi.org/10.1117/12.3011504

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KEYWORDS

Feature fusion

Convolution

Image fusion

Image quality

Air contamination

Atmospheric modeling

Image restoration

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