Rethinking the Elementary Function Fusion for Single-Image Dehazing

TL;DR

This paper introduces CL2S, a novel single-image dehazing network that replaces the traditional logarithmic function with a sine function to better model haze, resulting in improved detail preservation and color accuracy.

Contribution

It proposes a new dehazing network that replaces the logarithmic model with a sine function, enhancing the fit to complex haze distributions and improving dehazing performance.

Findings

CL2S outperforms existing methods on multiple datasets.

The sine function improves modeling of haze complexity.

Ablation studies validate the effectiveness of the proposed components.

Abstract

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.