Abstract

Thermal radiation effects can greatly degrade the image quality of uncooled infrared focal plane array detection systems. In this paper, we propose a thermal radiation effect correction network based on intra-block pyramid cross-scale feature extraction and fusion. First, an intra-block pyramid residual attention module is introduced to obtain fine-grained features from long-range IR images by extracting cross-scale local features within the residual block. Second, we propose a cross-scale gated fusion module to efficiently integrate the shallow and abstract features at multiple scales of the encoder and decoder through gated linear units. Finally, to ensure accurate correction of thermal radiation effects, we add double-loss constraints in the spatial–frequency domain and construct a single-input, multi-output network with multiple supervised constraints. The experimental results demonstrate that our proposed method outperforms state-of-the-art correction methods in terms of both visual quality and quantitative evaluation metrics.

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