Multiscale luminance adjustment-guided fusion for the dehazing of underwater images

Due to absorption and scattering effects in the underwater medium, acquired underwater images often suffer from hazy content and color casts, which lead to significant degradation of visual quality. In this work, a dehazing model is proposed; it is divided into three parts and effectively eliminates the problem of visual degradation caused by hazy content. The attenuation characteristics of light at different wavelengths are different, which usually leads to significant color bias in the obtained underwater images. Therefore, underwater images are first color corrected, namely by red channel compensation and white balance processing. Next, because the original hazy images are usually underexposed, we manually adjust the luminance of the images. The resulting multi-level luminance images are then fused using the Gaussian-Laplacian pyramid scheme to produce clear underwater images. To verify the validity of our method, we evaluate several representative underwater image datasets and compare our method with several advanced traditional methods and deep learning methods developed in recent years. Our method shows excellent results in both qualitative analysis and quantitative comparison.