IEEE Trans Image Process. 2016 Dec;25(12):5793-5805. doi: 10.1109/TIP.2016.2614160. Epub 2016 Sep 27.
This paper proposes a new image denoising algorithm based on adaptive signal modeling and regularization. It improves the quality of images by regularizing each image patch using bandwise distribution modeling in transform domain. Instead of using a global model for all the patches in an image, it employs content-dependent adaptive models to address the non-stationarity of image signals and also the diversity among different transform bands. The distribution model is adaptively estimated for each patch individually. It varies from one patch location to another and also varies for different bands. In particular, we consider the estimated distribution to have non-zero expectation. To estimate the expectation and variance parameters for every band of a particular patch, we exploit the nonlocal correlation in image to collect a set of highly similar patches as the data samples to form the distribution. Irrelevant patches are excluded so that such adaptively learned model is more accurate than a global one. The image is ultimately restored via bandwise adaptive soft-thresholding, based on a Laplacian approximation of the distribution of similar-patch group transform coefficients. Experimental results demonstrate that the proposed scheme outperforms several state-of-the-art denoising methods in both the objective and the perceptual qualities.
本文提出了一种基于自适应信号建模和正则化的新型图像去噪算法。它通过在变换域中使用逐带分布建模对每个图像块进行正则化来提高图像质量。该算法并非对图像中的所有块使用全局模型，而是采用与内容相关的自适应模型来处理图像信号的非平稳性以及不同变换带之间的多样性。针对每个块分别自适应估计分布模型。它在不同的块位置之间变化，并且在不同的频带中也有所不同。特别地，我们认为估计的分布具有非零期望。为了估计特定块每个频带的期望和方差参数，我们利用图像中的非局部相关性收集一组高度相似的块作为数据样本以形成分布。排除不相关的块，从而使这种自适应学习的模型比全局模型更准确。基于相似块组变换系数分布的拉普拉斯近似，最终通过逐带自适应软阈值处理来恢复图像。实验结果表明，所提出的方案在客观质量和感知质量方面均优于几种当前最先进的去噪方法。
