  This paper addresses the limitations of physical models in the current field
of image dehazing by proposing an innovative dehazing network (CL2S). Building
on the DM2F model, it identifies issues in its ablation experiments and
replaces the original logarithmic function model with a trigonometric (sine)
model. This substitution aims to better fit the complex and variable
distribution of haze. The approach also integrates the atmospheric scattering
model and other elementary functions to enhance dehazing performance.
Experimental results demonstrate that CL2S achieves outstanding performance on
multiple dehazing datasets, particularly in maintaining image details and color
authenticity. Additionally, systematic ablation experiments supplementing DM2F
validate the concerns raised about DM2F and confirm the necessity and
effectiveness of the functional components in the proposed CL2S model. Our code
is available at \url{https://github.com/YesianRohn/CL2S}, where the
corresponding pre-trained models can also be accessed.
