ERCO-Net: Enhancing Image Dehazing for Optimized Detail Retention

Image dehazing is a crucial preprocessing step in computer vision for enhancing image quality and enabling many downstream applications. However, existing methods often do not accurately restore hazy images while maintaining computational efficiency. To overcome this challenge, we propose ERCO-Net a new fusion framework that combines edge restriction and contextual optimization methods. By using boundary constraints, ERCO-Net extend the boundaries that help in protecting the edges and structures of an image. Contextual optimization impacts the final quality of the dehazed image by enhancing smoothness and coherence. We compare ERCO-Net with conventional approaches such as dark channel prior (DCP), All-in-one dehazing network (AoD), and Feature fusion attention network (FFA-Net). The comparative evaluation highlights the effectiveness of the proposed fusion method, providing significant improvement in image clarity, contrast, and colors. The combination of edge restriction and contextual optimization not only enhances the quality of dehazing but also decreases computational complexity, presenting a promising avenue for advancing image restoration techniques. The source code is available at https://github.com/FatimaAyub12/Image-Dehazing-.