自适应非局部数据保真项和双边总变分的图像去噪模型

Image denoising model with adaptive non-local data-fidelity term and bilateral total variation

针对常见去噪方法容易造成特定区域过度平滑、奇异结构残余噪声以及产生阶梯效应和对比度损失等问题,提出一种自适应非局部数据保真项和双边总变分的图像去噪模型,建立了自适应非局部正则化能量泛函和相应的变分框架。首先,对噪声图像利用自适应权值的非局部均值求得数据拟合项;其次,引入双边总变分正则化项,利用正则化系数来适度平衡数据拟合项和正则化项的影响;最后,通过能量函数最小化对不同的噪声统计快速求得最优解,从而达到降低残余噪声并纠正过度平滑的目的。通过理论分析和针对模拟噪声图像与真实噪声图像的实验结果表明,所提出的图像去噪模型能够较好地处理具有不同统计特性的图像噪声,与自适应非局部均值滤波去噪相比,所提算法的峰值信噪比(PSNR)值最多可以得到0.6 dB的改善;与全变分正则化图像去噪算法比较,所提算法的主观视觉效果明显更好,在去噪的同时图像纹理和边缘等细节信息保护得更好,PSNR值最多可以提高10 dB,而多尺度结构相似性度(MS-SSIM)指标可以提升0.3。因此,所提出的图像去噪模型可以在理论上更好地探讨如何合理处理噪声和图像内容本身的高频细节信息,在视频和图像分辨率提升等领域也具有良好的实际应用价值。

Aiming at the problems of over-smoothing, singular structure residual noise, contrast loss and stair effect of common denoising methods, an image denoising model with adaptive non-local data fidelity and bilateral total variation regularization was proposed, which provides an adaptive non-local regularization energy function and the corresponding variation framework. Firstly, the data fidelity term was obtained by non-local means filter with adaptive weighting method.Secondly, the bilateral total variation regularization was introduced in this framework, and a regularization factor was used to balance the data fidelity term and the regularization term. At last, the optimal solutions for different noise statistics were obtained by minimizing the energy function, so as to achieve the purpose of reducing residual noise and correcting excessive smoothing. The theoretical analysis and simulation results on simulated noise images and real noise images show that the proposed image denoising model can deal with different statistical noise in image, and the Peak-Signal-to-Noise Ratio（ PSNR）of it can be increased by up to 0. 6 d B when compared with the adaptive non-local means filter; when compared with the total variation regularization algorithm, the subjective visual effect of the proposed model was improved obviously and the details of image texture and edges was protected very well when denoising, and the PSNR was increased by up to 10 d B, the Multi-Scale Structural Similarity index（ MS-SSIM） was increased by 0. 3. Therefore, the proposed denoising model can theoretically better deal with the noise and the high frequency detail information of the image, and has good practical application value in the fields of video and image resolution enhancement.