目的 基于卷积神经网络（CNN）在图块级上实现的随机脉冲噪声（RVIN）降噪算法在执行效率方面较经典的逐像素点开关型降噪算法有显著优势，但降噪效果如何取决于能否对降噪图像受噪声干扰程度（噪声比例值）进行准确估计。为此，提出一种基于多层感知网络的两阶段噪声比例预测算法，达到自适应调用CNN预训练降噪模型获得最佳去噪效果的目的。方法 首先，对大量无噪声图像添加不同噪声比例的RVIN噪声构成噪声图像集合；其次，基于视觉码本（visual codebook）采用软分配（soft-assignment）编码法提取并筛选若干能反映噪声图像受随机脉冲噪声干扰程度的特征值构成特征矢量；再次，将从噪声图像上提取的特征矢量及对应的噪声比例分别作为多层感知网络的输入和输出训练噪声比例预测模型，实现从特征矢量到噪声比例值的映射（预测）；最后，采用粗精相结合的两阶段实现策略进一步提高RVIN噪声比例的预测准确性。结果 针对不同RVIN噪声比例的失真图像，从预测准确性、实际降噪效果和执行效率3个方面验证提出算法的性能和实用性。实验数据表明，本文算法在大多数噪声比例下的预测误差小于2%，降噪效果（PSNR指标）较其他主流降噪算法高24 dB，处理一幅大小为512×512像素的图像仅需3 s左右。结论 本文提出的RVIN噪声比例预测算法在各个噪声比例下具有鲁棒的预测准确性，在降噪效果和执行效率两个方面较经典的开关型RVIN降噪算法有显著提升，更具实用价值。

Objective The existing switching random-valued impulse noise (RVIN) removal algorithms mainly detect the noisy pixels of an image to be denoised by comparing the local image statistic with predefined thresholds. Then, denoising methods are combined to restore the detected noisy pixels in a pixel-wise manner, resulting in low execution efficiency. With regard to computational complexity, the convolutional neural network (CNN)-based denoising algorithms that were implemented at patch-level for RVIN exhibits a significant advantage over classical switching denoising algorithms that detect and remove RVIN pixel by pixel. However, the restoration performance of the CNN-based denoising algorithms remains limited to the accurate estimation of the distortion level of the given noisy image. In essence, the CNN-based denoising algorithm is still a non-blind method, wherein the optimal denoising effect can be only obtained by training a specific denoising model at a fixed noise level, thereby limiting practical application. For simplicity, the noise ratio can be treated as a measure of the distortion level of a noisy image by dividing the number of detected noisy pixels by the total number of image pixels. The CNN-based denoising methods can blindly and efficiently remove the RVIN with high quality by adaptively using the corresponding pre-trained denoisers in accordance with the estimated noise ratio. A two-stage noise ratio estimation algorithm based on multi-layer perceptron (MLP) was proposed in this paper to estimate the noise ratio precisely. Method Substantial clean images were first corrupted with RVIN at different ratios to form a set of noisy images. Then, the features that can reflect the distortion level of a noisy image were extracted and screened to form feature vector for each noisy image on the basis of the visual codebook and soft-assignment coding technology. Subsequently, the feature vectors and their corresponding noise ratios extracted from noisy images were used as the input and output of the multi-layer perceptron model, respectively, to train the noise ratio estimation model that maps a given feature vector to its corresponding noise ratio. Generally, numerous hidden layers are required in MLP architecture to obtain the ideal approximation function. However, the development of an MLP-based regression model with multi-hidden layers is difficult in convergence and training speed. Therefore, a coarse-to-fine two-stage strategy was used to improve the estimation accuracy further. Specifically, a relatively coarse noise ratio estimation model was trained across the entire range of noise ratio. Then, the noise ratio range was divided into several sub-ranges, indicating that the mapping range of the estimation model is diminished. Similarly, several fine noise ratio estimation models were trained in different noise ratio sub-ranges. Each subinterval overlaps with its adjacent subinterval to avoid the estimation inaccuracy at the subinterval extremities. In the prediction phase, a preliminary estimation is first obtained using the coarse estimation model. On this basis, the corresponding fine estimation model is used to predict the noise ratio further accurately. Result Comparison experiments were conducted to test the validity of the proposed method from three aspects, namely, estimation accuracy, denoising effect, and execution efficiency. The proposed method was initially compared with several classical noise detectors of RVIN denoising methods, such as PSMF, ROLD-EPR, ASVM, and ROR-NLM, to demonstrate the estimation accuracy. The number of detected noisy pixels was converted into noise ratio because the output result of the noise detectors of those compared switching denoising methods is the number of noisy pixels. Results show that the estimation error of the proposed method is less than 2% across different noise ratios, thereby showing stronger robustness than others. The feed-forward denoising convolutional neural network (DnCNN) algorithm designed for removing Gaussian noise was improved to manage RVIN removal for verifying the availability of the proposed method. In denoising effect comparison, the peak signal-to-noise ratio (PSNR), structural similarity (SSIM), and feature similarity (FSIM) were adopted as image quality assessment indexes. For the distorted images with different RVIN noise ratios, the PSNR values obtained by the improved DnCNN algorithm utilizing the proposed method increase by 2 dB more than that of others across the noise ratio range from 10% to 60%. Moreover, the FSIM values rank second for different noise ratios, whereas the SSIM values approximate the optimal results. With regard to qualitative visual evaluation, the improved DnCNN algorithm utilizing the proposed estimation model can generate a clear restored image with enhanced edge preservation. The improved DnCNN algorithm outperforms the switching RVIN removal methods in terms of execution efficiency, which takes only 3.8 s to restore an image of size 512×512 pixels. Conclusion Extensive experiments show that the estimation accuracy of the proposed MLP-based noise ratio estimation algorithm is robust across a wide range of noise ratios. With the proposed noise estimation model, the CNN-based RVIN removal algorithms can achieve optimal blind denoising by exploiting the closest matching model. Moreover, the improved DnCNN denoising algorithm with the noise ratio estimation module significantly outperforms the traditional switching RVIN denoising algorithm in terms of denoising effect and execution efficiency, thereby rendering it highly practical.