DehazeModel: Enhancing Image Clarity with an Encoder-Decoder CNN Approach

Abstract

We introduce DehazeModel, a Convolutional Neural Network (CNN) tailored for the purpose of enhancing single images by removing haze. DehazeModel comprises several components, including pre-processing, image dehazing, and post-processing modules. The pre-processing module, which is trainable, generates enhanced inputs featuring a wider array of characteristics compared to manually chosen pre-processing methods. The Image Dehazing module employs a novel encoder-decoder framework, overcoming common issues found in traditional multi-scale methods. Moreover, the post-processing module aids in minimizing artifacts in the resultant output. Experimental findings indicate that DehazeModel surpasses existing state-of-the-art techniques on the RESIDE dataset. Our experiments, conducted on both indoor and outdoor images, illustrate the robustness of our approach across various scenarios, independent of atmospheric scattering effects.