Medical image acquisition is often intervented by unwanted noise that corrupts the information content. This paper introduces an unsupervised medical image denoising technique that learns noise characteristics from the available images and constructs denoised images. It comprises of two blocks of data processing, viz., patch-based dictionaries that indirectly learn the noise and residual learning (RL) that directly learns the noise. The model is generalized to account for both 2D and 3D images considering different medical imaging instruments. The images are considered one-by-one from the stack of MRI/CT images as well as the entire stack is considered, and decomposed into overlapping image/volume patches. These patches are given to the patch-based dictionary learning to learn noise characteristics via sparse representation while given to the RL part to directly learn the noise properties. K-singular value decomposition (K-SVD) algorithm for sparse representation is used for training patch-based dictionaries. On the other hand, residue in the patches is trained using the proposed deep residue network. Iterating on these two parts, an optimum noise characterization for each image/volume patch is captured and in turn it is subtracted from the available respective image/volume patch. The obtained denoised image/volume patches are finally assembled to a denoised image or 3D stack. We provide an analysis of the proposed approach with other approaches. Experiments on MRI/CT datasets are run on a GPU-based supercomputer and the comparative results show that the proposed algorithm preserves the critical information in the images as well as improves the visual quality of the images.
翻译:医疗图像的获取往往被不必要噪音所干扰, 从而腐蚀信息内容。 本文引入了一种不受监督的医疗图像分解技术, 从现有图像中学习噪音特性, 并构建了无名图像。 它由两个数据处理区块组成, 即基于补丁的字典, 间接学习噪音和剩余学习( RL), 直接学习噪音。 模型被普遍化, 将2D 和 3D 图像纳入考虑不同医学成像仪的2D 和 3D 图像中。 图像被视为来自MRI/CT 图像堆以及整个堆的一对一, 并分解为重叠的图像/ 编叠。 这些补丁的字典将学习噪音特性, 通过稀释表示方式, 直接学习噪音特性。 K- Singal 值分解算法( K- SVD) 用于培训基于补丁基的字典的字典。 在另一方面, 利用拟议的深层残渣网络对补质量进行了训练。 在这两个部分中, 将精选的精选的图像/ 将最佳噪音转化为图像转换为每个图像在图像中显示/ 。