Traditional image fusion algorithms often struggle with slow processing speeds and suboptimal results, particularly when handling non-planar images. In this paper, we present a novel deep learning-based approach for panoramic image fusion. We begin by detailing our dataset construction and preprocessing techniques. To enhance the model’s capability with non-planar images, we apply the Thin Plate Spline (TPS) deformation algorithm, allowing effective panoramic fusion across complex image structures. The model architecture is based on a convolutional neural network (CNN) framework, integrated with up- and down-sampling modules to accurately and efficiently capture image features, resulting in higher-quality fusion outcomes. Experimental results demonstrate that this deep learning approach achieves faster fusion speeds and higher quality compared to traditional methods.
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