基于梯度指导的双通道深度压缩感知图像重建方法

Depth compression sensing image reconstruction method based on gradient guidance for two-channel

针对压缩感知重建图像边界部分模糊和畸变的问题,将图像的先验特性与深度学习网络相结合,提出了一种基于梯度指导的双通道深度压缩感知图像重建方法。该方法利用梯度图像和原始图像从边缘和纹理2个方面构建了双通道的深度网络模型。一方面,通过梯度分支来恢复高质量梯度图,为最后的重建图像提供额外的结构先验;另一方面,提出了梯度损失,图像的梯度约束有助于重建网络更专注于几何结构。在原图通道中采用混合卷积残差密集连接模块,扩大感受野的同时提取丰富的细节信息。实验结果表明,本方法与其他方法相比,取得了更高的重建质量,特别是在图像边界部分的恢复上有显著提升。

In order to solve the problem of blur and distortion in the boundary of compressed perceptual reconstruction image,the priori properties of image are combined with the depth learning network,a gradient-guided two-channel depth-compressed perceptual image reconstruction method is proposed.This method uses gradient image and original image to construct a two-channel depth network model from two aspects of edge and texture.On the one hand,the gradient branches are used to restore the high quality gradient map,which provides an additional structural prior for the final reconstructed image,the gradient constraint of the image helps to reconstruct the network focusing more on the geometric structure.In the channel of the original image,the mixed convolution residuals are connected tightly to expand the receptive field and extract rich detail information.Experimental results show that the proposed method achieves better reconstruction quality than other methods,especially in the restoration of image boundary.