IEEE Trans Image Process. 2014 Jun;23(6):2459-72. doi: 10.1109/TIP.2014.2316423. Epub 2014 Apr 10.
Natural image statistics plays an important role in image denoising, and various natural image priors, including gradient-based, sparse representation-based, and nonlocal self-similarity-based ones, have been widely studied and exploited for noise removal. In spite of the great success of many denoising algorithms, they tend to smooth the fine scale image textures when removing noise, degrading the image visual quality. To address this problem, in this paper, we propose a texture enhanced image denoising method by enforcing the gradient histogram of the denoised image to be close to a reference gradient histogram of the original image. Given the reference gradient histogram, a novel gradient histogram preservation (GHP) algorithm is developed to enhance the texture structures while removing noise. Two region-based variants of GHP are proposed for the denoising of images consisting of regions with different textures. An algorithm is also developed to effectively estimate the reference gradient histogram from the noisy observation of the unknown image. Our experimental results demonstrate that the proposed GHP algorithm can well preserve the texture appearance in the denoised images, making them look more natural.
自然图像统计在图像去噪中起着重要作用，各种自然图像先验，包括基于梯度的、基于稀疏表示的和基于非局部自相似性的，已经被广泛研究和应用于噪声去除。尽管许多去噪算法取得了巨大的成功，但它们在去除噪声时往往会平滑精细的图像纹理，从而降低图像的视觉质量。针对这个问题，本文提出了一种纹理增强的图像去噪方法，通过使去噪图像的梯度直方图接近原始图像的参考梯度直方图。给定参考梯度直方图，我们开发了一种新的梯度直方图保持（GHP）算法，用于在去除噪声的同时增强纹理结构。针对具有不同纹理的区域组成的图像，提出了两种基于区域的 GHP 变体。还开发了一种算法，可从未知图像的噪声观测中有效地估计参考梯度直方图。实验结果表明，所提出的 GHP 算法可以很好地保持去噪图像中的纹理外观，使其看起来更加自然。
