At present, most denoising algorithms cannot determine whether a pixel is noise, but these use the same rules to process all pixels. Most denoising methods will filter out the original image information when they deal with images with more details or little difference between the subject and the background. In order to improve the above shortcomings, a two-stage image denoising algorithm of noise localization in this paper is proposed.Firstly, the thresholds \( {T}_1^{\prime } \) and \( {T}_2^{\prime } \) are extracted according to the image gray value distribution. Image edge information is removed and saved by edge extraction, this gets an edgeless greyscale image. Secondly, singular value decomposes the edgeless image to obtain the singular value matrix, the percentage threshold η is used to reduce the singular value matrix.The coarse noise filtering is performed by the inverse matrix decomposition. Again, the adaptive thresholds T₁ and T₂ are calculated with the histogram, the image is divided into “Dark Area”, “Gray Area” and “Light Area”. Then, a superpixel-like algorithm is introduced to determine and remove the noise accurately in three regions. Finally, the image edges are combined with the denoised image. By analyzing the denoising image and comparing the peak signal-to-noise ratio (PSNR) and time of the result in many images, it is verified that the proposed algorithm has a better denoising effect than many other denoising algorithms.

目前，大多数降噪算法无法确定像素是否为噪声，但是这些算法使用相同的规则来处理所有像素。当大多数降噪方法处理的图像具有更多细节或对象与背景之间的差异很小时，它们会滤除原始图像信息。为了克服上述缺点，提出了一种噪声定位的两阶段图像去噪算法。首先，阈值\（{T} _1 ^ {\ prime} \）和\（{T} _2 ^ {根据图像灰度值分布提取\ prime} \）。图像边缘信息通过边缘提取被删除并保存，这将获得无边缘的灰度图像。其次，奇异值分解无边缘图像以获得奇异值矩阵，百分比阈值η用于简化奇异值矩阵。通过逆矩阵分解执行粗噪声滤波。再次，利用直方图计算自适应阈值T ₁和T ₂，将图像划分为“暗区域”，“灰色区域”和“亮区域”。然后，引入了一种超像素类算法来准确确定并消除三个区域中的噪声。最后，将图像边缘与去噪图像合并。通过对去噪图像进行分析，并比较许多图像中的峰值信噪比（PSNR）和结果时间，可以证明该算法具有比其他去噪算法更好的去噪效果。