A Two-Stage Noise Level Estimation Using Automatic Feature Extraction and Mapping Model

Abstract

In this letter, a two-stage noise level estimation (NLE) algorithm that jointly exploited automatic feature extraction and mapping model was proposed. In contrast to existing NLE algorithms using hand-crafted features, we first utilized convolutional neural network-based model to automatically extract the noise level-aware features (NLAFs) in form of feature vector to characterize the distortion degree of a noisy image, i.e., noise level. Then, the NLAF vector was directly mapped to its corresponding noise level via pretrained mapping model, obtaining a fast and reliable NLE algorithm. Extensive experimental results show that the proposed NLE algorithm works well for a wide range of noise levels, showing a good compromise between speed and accuracy.