研究目的
To address the residual nonuniformity response, ghosting artifacts, and over-smooth effects in existing nonuniformity correction methods for infrared focal plane array sensors.
研究成果
The proposed NUC method effectively reduces nonuniformity, suppresses ghosting artifacts, and avoids over-smooth effects with faster convergence, as validated by experiments on simulated and real data, suggesting its superiority over existing methods.
研究不足
The method may be sensitive to parameter tuning (e.g., spatial weight α and balance scalar λ), and computational load could be high for large sequences; further optimization and real-time application challenges are potential areas for improvement.
1：Experimental Design and Method Selection:
The study employs a spatiotemporal adaptive nonuniformity correction algorithm with bilateral total variation regularization, using steepest descent optimization and neural network-based correction.
2：Sample Selection and Data Sources:
Simulated sequences (Sequence 1: 1600 frames from RTD3172C imager; Sequence 2: 4000 frames from A615 camera) and a real infrared sequence (Sequence 3: 1970 frames from ULIS Pico384 camera) are used.
3：List of Experimental Equipment and Materials:
Infrared cameras and sequences as specified.
4：Experimental Procedures and Operational Workflow:
Parameters are initialized, and iterative correction is applied with adaptive learning rates and filters; performance is assessed using PSNR and roughness index.
5：Data Analysis Methods:
Quantitative analysis with PSNR and roughness index, and qualitative visual inspection of corrected images.
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