为了解决图像压缩感知重建研究领域中通过有效的图像先验信息重构与原图相似性高且保留细节消除伪影的高质量图像的问题, 针对不足采样的K空间数据, 在经典的CNN算法CBDNet算法的基础上, 通过融合深度学习先验信息及传统图像恢复各自优势的方法, 研究了基于深度神经网络去噪先验和BM3D块压缩感知算法的混合式重构算法. 该算法采用交互式方法训练多尺度残差网络抑制噪声水平, 借优化选择的方式将深度学习与传统块匹配多尺度结合以提取图像不同尺度的特征数据从而实现抑制伪影、快速重建高质量MRI. 实结果表明深度学习结合BM3D在MR图像重构领域能够有效降低伪影保留细节信息, 加强重构效果. 与此同时, 通过采用GPU的加速运算, 算法的计算复杂度较使用单一算法并未增加很多. 可见基于卷积盲降噪的混合式核磁共振成像效果更佳.
In the field of image compression perceptual reconstruction, high-quality images are reconstructed with high similarity to the original image, and details are retained to eliminate artifacts through effective image prior information reconstruction. Thus, aiming at the K-space data with insufficient sampling, based on the classic CNN algorithm CBDNet algorithm, this study adopts the method to combine the advantages of fusing deep learning prior information and traditional image restoration. Meanwhile, a hybrid reconstruction algorithm based on priori denoising of deep neural network and compressed sensing algorithm of BM3D block is studied. The algorithm employs an interactive method to train a multi-scale residual network to suppress noise levels and combines deep learning with the multi-scale matching of traditional blocks to extract image feature data at different scales through optimal selection, thus suppressing artifacts and quickly reconstructing high-quality MRI. The experimental results show that deep learning combined with BM3D can reduce artifacts and retain details in MR image reconstruction, enhancing the reconstruction effect. Additionally, the computational complexity of the algorithm is not much more than that of the single algorithm by the GPU accelerated operation. It can be seen that the hybrid MRI based on convolution blind denoising has a better effect.