结合降噪卷积神经网络和条件生成对抗网络的图像双重盲降噪算法

针对图像降噪中降噪效果差、计算效率低的问题，提出了一种结合降噪卷积神经网络（DnCNN）和条件生成对抗网络（CGAN）的图像双重盲降噪算法。首先，使用改进的DnCNN模型作为CGAN的生成器来对加噪图片的噪声分布进行捕获；其次，将剔除噪声分布后的加噪图片和标签一同送入判别器进行降噪图像的判别；然后，利用判别结果对整个模型的隐层参数进行优化；最后，生成器和判别器在博弈中达到平衡，且生成器的残差捕获能力达到最优。实验结果表明，在Set12数据集上，当噪声水平分别为15、25、50时：所提算法与DnCNN算法相比，基于像素点间误差评价指标，其峰值信噪比（PSNR）值分别提升了1.388 dB、1.725 dB、1.639 dB；所提算法与三维块匹配（BM3D）、加权核范数最小化（WNNM）、DnCNN、收缩场级联（CSF）和一致性神经网络（CSNET）等现有算法相比，结构相似性（SSIM）评价指标值平均提升了0.000 2~0.104 1。实验结果验证了所提算法的优越性。

Image double blind denoising algorithm combining with denoising convolutional neural network and conditional generative adversarial net

In order to solve the problems of poor denoising effect and low computational efficiency in image denoising, a double blind denoising algorithm based on Denoising Convolutional Neural Network (DnCNN) and Conditional Generative Adversarial Net (CGAN) was proposed. Firstly, the improved DnCNN model was used as the CGAN generator to capture the noise distribution of the noisy image. Secondly, the noisy image after eliminating the noise distribution and the tag were sent to the discriminator to distinguish the noise reduction image. Thirdly, the results of discrimination were used to optimize the hidden layer parameters of the whole model. Finally, a balance between the generator and the discriminator was achieved in the game, and the generator's residual capture ability was optimal. Experimental results show that on Set12 dataset, when the noise levels are 15, 25, 50 respectively:compared with the DnCNN algorithm, the proposed algorithm has the Peak Signal-to-Noise Ratio (PSNR) increased by 1.388 dB, 1.725 dB and 1.639 dB respectively based on the error evaluation index between pixel points. Compared with the existing algorithms such as Block Matching 3D (BM3D), Weighted Nuclear Norm Minimization (WNNM), DnCNN, Cascade of Shrinkage Fields (CSF) and ConSensus neural NETwork (CSNET), the proposed algorithm has the index value of Structural SIMilarity (SSIM) improved by 0.000 2 to 0.104 1 on average based on the evaluation index of structural similarity. The above experimental results verify the superiority of the proposed algorithm.