Fully Non-Homogeneous Atmospheric Scattering Modeling with Convolutional Neural Networks for Single Image Dehazing

Understand

In recent years, single image dehazing models (SIDM) based on atmospheric scattering model (ASM) have achieved remarkable results.

• However, it is noted that ASM-based SIDM degrades its performance in dehazing real world hazy images due to the limited modelling ability of ASM where the atmospheric light factor (ALF) and the angular scattering coefficient (ASC) are assumed as constants for one image.
• Obviously, the hazy images taken in real world cannot always satisfy this assumption.
• Such generating modelling mismatch between the real-world images and ASM sets up the upper bound of trained ASM-based SIDM for dehazing.