Cao Jia, Qiang Zhenping, Lin Hong, He Libo, Dai Fei
College of Big Data and Intelligent Engineering, Southwest Forestry University, Kunming 650224, China.
Information Security College, Yunnan Police College, Kunming 650221, China.
Sensors (Basel). 2023 Aug 18;23(16):7265. doi: 10.3390/s23167265.
We propose an improved BM3D algorithm for block-matching based on UNet denoising network feature maps and structural similarity (). In response to the traditional BM3D algorithm that directly performs block-matching on a noisy image, without considering the deep-level features of the image, we propose a method that performs block-matching on the feature maps of the noisy image. In this method, we perform block-matching on multiple depth feature maps of a noisy image, and then determine the positions of the corresponding similar blocks in the noisy image based on the block-matching results, to obtain the set of similar blocks that take into account the deep-level features of the noisy image. In addition, we improve the similarity measure criterion for block-matching based on the Structural Similarity Index, which takes into account the pixel-by-pixel value differences in the image blocks while fully considering the structure, brightness, and contrast information of the image blocks. To verify the effectiveness of the proposed method, we conduct extensive comparative experiments. The experimental results demonstrate that the proposed method not only effectively enhances the denoising performance of the image, but also preserves the detailed features of the image and improves the visual quality of the denoised image.
我们提出了一种基于UNet去噪网络特征图和结构相似性的改进型基于块匹配的BM3D算法。针对传统BM3D算法直接对噪声图像进行块匹配，未考虑图像深层特征的问题，我们提出了一种对噪声图像特征图进行块匹配的方法。在该方法中，我们对噪声图像的多个深度特征图进行块匹配，然后根据块匹配结果确定噪声图像中对应相似块的位置，以获得考虑了噪声图像深层特征的相似块集合。此外，我们基于结构相似性指数改进了块匹配的相似性度量准则，该准则在充分考虑图像块的结构、亮度和对比度信息的同时，考虑了图像块中逐像素的差值。为验证所提方法的有效性，我们进行了大量的对比实验。实验结果表明，所提方法不仅有效提高了图像的去噪性能，还保留了图像的细节特征，提升了去噪后图像的视觉质量。
