Speckles destroy the texture details of synthetic aperture radar (SAR) images, thereby constraining their high-precision application. Speckle suppression and edge preservation are two aspects that need to be balanced in despeckling. Although a conventional anisotropic diffusion (AD) filter can theoretically achieve this balance, it still triggers many edge losses. To better improve the balance, a novel AD filter based on the pixel difference function (PDF) and local entropy (LE) is proposed. The proposed filter utilizes a PDF to update the original diffusion function of the AD filter and introduces LE to recover the edge loss from the ratio image generated by noisy and filtered images. In addition, a neighborhood weighting approach and a new adaptive iterative rule are proposed for better AD filtering. Simulated data and real SAR images were applied to evaluate the performance of the proposed algorithm. Experimental results show that the proposed filter both effectively smooths speckles and reduces edge loss. Furthermore, the effectiveness and superiority of the proposed method were confirmed by comparison with other state-of-the-art methods.

中文翻译：

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像素差分函数和基于局部熵的散斑减少各向异性扩散

散斑破坏了合成孔径雷达（SAR）图像的纹理细节，从而限制了它们的高精度应用。斑点抑制和边缘保留是去斑点中需要平衡的两个方面。尽管传统的各向异性扩散 (AD) 滤波器理论上可以实现这种平衡，但它仍然会触发许多边缘损失。为了更好地改善平衡，提出了一种基于像素差分函数（PDF）和局部熵（LE）的新型AD滤波器。所提出的滤波器利用 PDF 来更新 AD 滤波器的原始扩散函数，并引入 LE 从噪声和滤波图像生成的比率图像中恢复边缘损失。此外，提出了一种邻域加权方法和一种新的自适应迭代规则，以实现更好的 AD 过滤。模拟数据和真实 SAR 图像用于评估所提出算法的性能。实验结果表明，所提出的滤波器既能有效地平滑斑点，又能减少边缘损失。此外，通过与其他最先进的方法进行比较，证实了所提出方法的有效性和优越性。