Flatness loss for image dehazing
Chenyu Zhang, Qihong Ye, Hongming Chen, Xiaoshuang Wang · 2023
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.