深度非对称跳跃连接的图像降噪方法

图像降噪可有效地改善图像质量,提升感官效果,也是图像特征提取与理解的前提.针对目前比较流行的卷积神经网络降噪方法中顺序连接的卷积层-反卷积层会使图像在梯度反传过程中逐渐弱化图像噪声的学习问题,提出一种深度非对称跳跃连接的方法用于图像降噪.该方法设计多组非对称跳跃连接卷积-反卷积算子,以有效学习图像细节及噪声信息,并对不同深度的卷积操作进行权重量化,以加强图像降噪及恢复;通过非对称跳跃连接可使图像噪声信息能够直接反传到对应的多个卷积层中,对梯度扩散有良好的抑制作用.采用伯克利分割数据集BSD300进行实验的结果表明,文中算法比基准方法在结构相似性(SSIM)和峰值信噪比(PSNR)2种指标上都有提升.

An Image Denoising Method Using Deep Asymmetrical Skip Connection

Image denoising can effectively improve image quality and sensory effect,and is also the premise of image feature extraction and understanding.For the current popular convolution neural network denoising methods,sequentially connected convolution-deconvolution layer will gradually weaken the image noise in the gradient back propagation process,a method of deep asymmetrical skip connection is proposed for image denoising.In this method,several asymmetrical skip convolution-deconvolution operators are designed to effectively learn image details and noise information,the weights of convolution operations with different depths are quantized to enhance image denoising and restoration.The asymmetrical skip connection can make the image noise information be transmitted directly back to the corresponding convolution layers,which has a good inhibition on gradient diffusion.Experiments on a Berkeley Segmentation Dataset BSD300 show that the proposed algorithm can improve both structural similarity(SSIM)and peak signal-to-noise ratio(PSNR)compared with the benchmark method.