浅层CNN网络构建的噪声比例估计模型  

A shallow CNN-based noise ratio estimation model

作　　者：徐少平[1] 林珍玉 李崇禧 崔燕 刘蕊蕊 Xu Shaoping;Lin Zhenyu;Li Chongxi;Cui Yan;Liu Ruirui(School of Information Engineering,Nanchang University,Nanchang 330031,China)

机构地区：[1]南昌大学信息工程学院,南昌330031

出　　处：《中国图象图形学报》2020年第7期1344-1355,共12页Journal of Image and Graphics

基　　金：国家自然科学基金项目(61662044,61163023,51765042);江西省自然科学基金项目(20171BAB202017)。

摘　　要：目的利用深度卷积神经网络(deep convolutional neural network,DCNN)构建的非开关型随机脉冲噪声(random-valued impulse noise,RVIN)降噪模型在降噪效果和执行效率上均比主流的开关型RVIN降噪算法更有优势,但在实际应用中,这类基于训练(数据驱动)的降噪模型,其性能却受制于能否对待降噪图像受噪声干扰的严重程度进行准确的测定(即存在数据依赖问题)。为此,提出了一种基于浅层卷积神经网络的快速RVIN噪声比例预测(noise ratio estimation,NRE)模型。方法该预测模型的主要任务是检测待降噪图像中的噪声比例值并将其作为反映图像受噪声干扰严重程度的指标,依据NRE预测模型的检测结果可以自适应调用相应预先训练好的特定区间DCNN降噪模型,从而快速且高质量地完成图像降噪任务。结果分别在10幅常用图像和50幅纹理图像两个测试集上进行测试,并与现有的主流RVIN降噪算法中的检测模块进行对比。在常用图像测试集上,本文所提出的NRE预测模型的预测准确性最高。相比于噪声比例预测精度排名第2的算法,NRE预测模型在噪声比例预测值均方根误差上低0.6%~2.4%。在50幅纹理图像测试集上,NRE模型的均方根误差波动范围最小,表明其稳定性最好。通过在1幅大小为512×512像素图像上的总体平均执行时间来比较各个算法执行效率的优劣,NRE模型执行时间仅为0.02 s。实验数据表明:所提出的NRE预测模型在受各种不同噪声比例干扰的自然图像上均可以快速而稳定地测定图像中受RVIN噪声干扰的严重程度,非盲的DCNN降噪模型与其联用后即可无缝地转化为盲降噪算法。结论本文RVIN噪声比例预测模型在各个噪声比例下具有鲁棒的预测准确性,与基于DCNN的非开关型RVIN深度降噪模型配合使用后能妥善解决DCNN网络模型固有的数据依赖问题。Objective Nonswitching random-valued impulse noise(RVIN)denoisers built with deep convolution neural networks(DCNNs)have many advantages compared with the mainstream switching RVIN removal algorithms in terms of denoising effect and execution efficiency.However,the performance of training-based(data-driven)denoisers in practical applications experiences inaccurate measurement of the distortion level of a given image to be denoised(data dependency problem).A fast noise ratio estimation(NRE)model based on shallow CNN(SCNN)was proposed in this study.Method The noise ratio reflecting the distortion level of a given noisy image was estimated using the proposed NRE model.On the basis of the estimated noise ratio,the corresponding DCNN-based denoiser trained at a specific interval of noise ratios can be adaptively exploited to efficiently remove RVIN with high denoised image quality.Result Comparison experiments were conducted to test the validity of the proposed NRE model from three aspects,namely,estimation accuracy,denoising effect,and execution efficiency.We utilized the NRE model to estimate the noise ratios of given noisy images and compared the results with the existing classical RVIN noise reduction algorithms(including PSMF(progressive switching median filter),ROLD-EPR(rank-ordered logarithmic difference edge-preserving regularization),ASWM(adaptive switching median),ROR-NLM(robust outlyingness ratio nonlocal means),MLP-EPR(multilayer perceptron edge-preserving regularization))to verify its estimation accuracy.Considering that these competing algorithms detect noisy pixels in a pixelwise manner,the number of pixels identified as noise was divided by the total number of pixels in the entire image,and the detection results were transformed as noise ratio to facilitate comparison with the proposed NRE model.Two image sets were used,where the first image set included Lena,House,Peppers,Couple,Hill,Barbara,Boat,Man,Cameraman,and Monarch images,and the second image set was randomly selected from the Business Structur

关 键 词：随机脉冲噪声 噪声比例估计 浅层卷积神经网络 非逐点模式 执行效率 盲降噪 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]