A Feature Fusion Attention Network is a deep learning architecture designed for image restoration tasks, such as single-image dehazing, by adaptively integrating multi-scale visual features through attention mechanisms. The network typically employs attention modules that combine channel attention with pixel-level spatial attention, allowing it to dynamically assign greater importance to highly informative feature channels and unevenly degraded image regions. By combining these attention mechanisms with local residual learning structures, the architecture enables less critical or low-frequency information to bypass deep processing layers while adaptively weighting and merging shallow and deep representations. This selective fusion of features across different levels enhances the network capacity to effectively remove visual artifacts and reconstruct clean, high-fidelity images.
