Stripe non-uniformity severely affects the quality of infrared images. It is challenging to remove stripe noise in low-texture images without blurring the details. We propose a single-frame image stripe correction algorithm that removes infrared noise while preserving image details. Firstly, wavelet transform is used for multi-scale analysis of the image. At the same time, Total variation model is used for small window to smooth the original image. The small-scale total variation model can well preserve the edge information of the image, but it will leave stripe noise. Therefore, according to the prior knowledge of the vertical component of the stripe noise, the spatial filtering is finally performed: the smoothed image is used as the guide image for the stripe noise denoising. It is possible to prevent the lead filter from mistaking the strong stripe noise as edge detail, resulting in corrected image residual streak noise. The algorithm is systematically evaluated by experiments on simulated images and original infrared images, as well as compared with the current advanced infrared stripe non-uniformity correction algorithms. It is proved that our algorithm can better eliminate stripe noise and preserve edge details.

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基于小波分解和总变化指导滤波的红外条纹校正算法

条纹不均匀严重影响红外图像的质量。在不模糊细节的情况下消除低纹理图像中的条纹噪声是一项挑战。我们提出了一种单帧图像条纹校正算法，该算法可在保留图像细节的同时消除红外噪声。首先，小波变换用于图像的多尺度分析。同时，Total Variation模型用于小窗口以平滑原始图像。小规模的总变异模型可以很好地保留图像的边缘信息，但是会留下条纹噪声。因此，根据条带噪声的垂直分量的先验知识，最终执行空间滤波：将平滑图像用作条带噪声去噪的指导图像。可以防止超前滤波器将强条纹噪声误认为边缘细节，从而导致校正后的图像残留条纹噪声。通过对模拟图像和原始红外图像进行实验，对该算法进行了系统评估，并与当前先进的红外条纹非均匀性校正算法进行了比较。证明了我们的算法可以更好地消除条纹噪声并保留边缘细节。