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C2AIR: Consolidated Compact Aerial Image Haze Removal
Aerial image haze removal deals with improving the visibility and quality of images captured from aerial platforms, such as drones and satellites. Aerial images are commonly used in various applications such as environmental monitoring, and disaster response. These applications usually require cleaner data for accurate functioning. However, atmospheric conditions such as haze or fog can significantly degrade the quality of these images, reducing their contrast, color saturation, and sharpness, making it difficult to extract meaningful information from them. Existing methods rely on computationally heavy and haze density (light, moderate, dense) specific architectures for aerial image dehazing. In light of these limitations, we propose a novel lightweight and consolidated approach for aerial image dehazing. In this approach, we propose Density Aware Query Modulated Block for learning weather degradations in input features and guiding the restoration process. Further, we propose Cross Collaborative Feed-Forward Block for learning to restore varying sizes of the structures in the input images. Finally, we propose Gated Adaptive Feature Fusion block to achieve inter-scale and intra-feature attentive fusion, effective for aerial image restoration. Extensive analysis on benchmark aerial image dehazing datasets and real-world images, along with detailed ablation studies validate the effectiveness of the proposed approach. Further, we have analysed our method for other restoration task such as underwater image enhancement to experiment its wide applicability. The code is available at https: //github.com/AshutoshKulkarni4998/C2AIR.