BM3D is a state-of-the-art image denoising method. Its denoised results in the regions with strong edges can often be better than in the regions with smooth or weak edges, due to more accurate block-matching for the strong-edge regions. So using adaptive block sizes on different image regions may result in better image denoising. Based on these observations, in this paper, we first partition each image into regions belonging to one of the three morphological components, i.e., contour, texture, and smooth components, according to the regional energy of alternating current (AC) coefficients of discrete cosine transform (DCT). Then, we can adaptively determine the block size for each morphological component. Specifically, we use the smallest block size for the contour components, the medium block size for the texture components, and the largest block size for the smooth components. To better preserve image details, we also use a multi-stage strategy to implement image denoising, where every stage is similar to the BM3D method, except using adaptive sizes and different transform dimensions. Experimental results show that our proposed algorithm can achieve higher PSNR and MSSIM values than the BM3D method, and also better visual quality of denoised images than by the BM3D method and some other existing state-of-the-art methods.

中文翻译：

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使用形态和尺寸自适应块匹配变换域过滤进行图像去噪。

BM3D 是一种最先进的图像去噪方法。由于对强边缘区域的块匹配更准确，其在具有强边缘的区域中的去噪结果通常比在具有平滑或弱边缘的区域中更好。因此，在不同的图像区域上使用自适应块大小可能会产生更好的图像去噪效果。基于这些观察，在本文中，我们首先根据离散余弦的交流（AC）系数的区域能量将每个图像划分为属于三个形态分量（即轮廓、纹理和平滑分量）之一的区域变换（DCT）。然后，我们可以自适应地确定每个形态成分的块大小。具体来说，我们对轮廓分量使用最小块大小，对纹理分量使用中等块大小，对平滑分量使用最大块大小。为了更好地保留图像细节，我们还使用多阶段策略来实现图像去噪，其中每个阶段与BM3D方法类似，除了使用自适应尺寸和不同的变换维度。实验结果表明，我们提出的算法可以实现比 BM3D 方法更高的 PSNR 和 MSSIM 值，并且与 BM3D 方法和其他一些现有的最先进方法相比，去噪图像的视觉质量也更好。