Saturation energy restoration technology for infrared point targets

In the imaging process of infrared cameras, the strong radiation energy of certain targets often exceeds the quantization ability of the detector, resulting in pixel intensity saturation and distortion. This saturation can lead to the loss of reliable target energy information, thereby hindering precise radiation analysis, feature extraction, and recognition in various optical applications. To address this issue, this study simulated the target imaging link and designed an effective method to restore the energy distribution of saturated point targets, in order to improve measurement accuracy and application reliability. For stationary point targets, a two-dimensional Gaussian distribution model is used and a two-dimensional surface fitting method is employed for restoration. In addition, this study also designed a recovery model for moving point targets, the “Gaussian-convolved line-segment kernel” model, aimed at eliminating the influence of tailing caused by target motion on energy recovery. The model considers tailing caused by target motion and better compensates for energy distortion in dynamic imaging scenes. After testing, the surface fitting method proposed in this study achieved a relative error of less than 5% in target energy restoration and a relative error of less than 20% in restoring the maximum target energy. The experimental results show that this method provides accurate reconstruction of saturated point targets while maintaining high fidelity of spatial distribution. The surface fitting method can reliably recover saturated point targets in both stationary and moving scenes, and has important application value in target recognition and classification. It is crucial for accurate detection, threat assessment, and rapid decision-making.