噪声图像质量评价主要包含两部分内容:噪声图像失真度评价和噪声类型识别以

Noise image quality evaluation mainly includes two parts: noise image distortion evaluation and noise type recognition and noise intensity estimation. The distortion degree evaluation belongs to the category of image quality evaluation. The two aspects are introduced below. <br>This article focuses on noise type identification and parameter estimation. Here we only introduce four classic evaluation indicators including mean square error (MSE), peak signal-to-noise ratio (PSNR), visual signal-to-noise ratio (VSNR), structural similarity (SSIM), and specific As follows: <br>The mean value is used as the brightness estimate in the image structure similarity, the standard deviation is used as the contrast estimate, and the covariance is used as a measure of the structure similarity. The calculation formula for the structural similarity between the original image and the processed image is

Noise image quality evaluation mainly consists of two parts: noise image distortion evaluation and noise type recognition, as well as noise intensity estimation, in which distortion evaluation belongs to the category of image quality evaluation, the following two aspects are introduced.<br>Focusing on noise type recognition and parameter estimation, this paper introduces only four classical evaluation indicators, including mean square error (MSE), peak signal-to-noise ratio (PSNR), visual signal-to-noise ratio (VSNR), and structural similarity (SSIM), as follows:<br>Among them, the mean is used as the brightness estimate in the image structure similarity, the standard deviation is used as the contrast estimate, and the covariance is used as the measure of the structural similarity. The structural similarity between the original image and the processed image is calculated as a formula

The quality evaluation of noise image mainly includes two parts: the evaluation of distortion degree of noise image, the recognition of noise type and the estimation of noise intensity. Among them, the evaluation of distortion degree belongs to the category of image quality evaluation. The following two aspects are introduced respectively.<br>This paper focuses on the identification of noise types and parameter estimation. Here, only four classical evaluation indexes are introduced, including mean square error (MSE), peak signal-to-noise ratio (PSNR), visual signal-to-noise ratio (vsnr), structural similarity (SSIM), as follows:<br>The average value is used as brightness estimation, standard deviation is used as contrast estimation, and covariance is used as the measure of structure similarity. The formula for calculating the structural similarity between the original image and the processed image is<br>