Abstract: To resolve the problem that the existing single image-based noise level estimation (SNLE) algorithms are of poor anti-interference ability and low execution efficiency, a multi-image based noise level estimation (MNLE) algorithm using prior knowledge of similar images is proposed. Specifically, a set of distorted images is first constructed by adding different pre-defined noise levels to some representative noise-free images, and several natural statistical values extracted from each distorted image are used to form the noise level-aware feature vector. Then, the feature vector extracted from each distorted image and its corresponding noise level are used to construct a sample database. During the noise level estimation stage, the feature vector of an image to be estimated is extracted by the same method as the preparation stage, and several feature vectors similar to the extracted feature vector and their corresponding noise levels are chosen from the sample database. As such, the noise level of the image to be estimated is estimated with a weighted average approach. Extensive experimental results show that the proposed MNLE algorithm not only is of high efficiency but also has stable prediction accuracy at high, medium, and low noise levels. For the Gaussian noise mixed with impulse noise or Poisson noise, it also has good anti-interference ability.