In this paper, we present a hybrid denoising algorithm dedicated to division-of-focal plane (DoFP) polarization images. The proposed algorithm, centered around the Block-Matching and 3D Filtering (BM3D) and K-times Singular Value Decomposition (KSVD) denoising algorithms, is capable of significantly enhancing the grouping step in the second round of collaborative filtering by purifying the “Semi-Filtered” image yielded by the first round of collaborative filtering. To achieve this, the BM3D denoising method’s chain of operation is broken, and the “Semi-Filtered” image is passed through a round of KSVD denoising method before the second round of collaborative filtering is conducted. According to our extensive experimental results, the proposed algorithm visually outperforms the state-of-the-art BM3D denoising algorithm and a wide range of other denoising algorithms for DoFP polarization images. Quantitative results presented using Peak-Signal-to-Noise-Ratio (PSNR) and Structural Similarity Index (SSIM) Index metrics further highlight the superior performance of the proposed algorithm.

中文翻译：

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DoFP偏振图像中高斯噪声的BM3D和KSVD混合去噪算法

在本文中，我们提出了一种专用于焦平面分割 (DoFP) 偏振图像的混合去噪算法。所提出的算法以块匹配和 3D 过滤 (BM3D) 和 K 次奇异值分解 (KSVD) 去噪算法为中心，能够通过净化“半第一轮协同过滤产生的“过滤”图像。为此，BM3D去噪方法的操作链被打破，“半滤波”图像在进行第二轮协同过滤之前通过一轮KSVD去噪方法。根据我们广泛的实验结果，所提出的算法在视觉上优于最先进的 BM3D 去噪算法和用于 DoFP 偏振图像的各种其他去噪算法。使用峰值信噪比 (PSNR) 和结构相似性指数 (SSIM) 指数指标呈现的定量结果进一步突出了所提出算法的卓越性能。