Deep Image Prior Denoising Model Using Relatively Clean Image Space Search
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摘要: 鉴于深度图像先验(DIP)降噪模型的性能高度依赖于目标图像所确定的搜索空间，该文提出一种新的基于近清图像空间搜索策略的改进降噪模型。首先，使用当前两种主流有监督降噪模型对同一场景下两张噪声图像分别进行降噪，所获得两张降噪后图像称为近清图像；其次，采用随机采样融合法将两张近清图像融合后作为网络输入，同时以两张近清图像替换噪声图像作为双目标图像以更好地约束搜索空间，进而在更为接近参考图像的空间范围内搜索可能的图像作为降噪后图像；最后，将原DIP模型的多尺度UNet网络简化为单尺度模式，同时引入Transformer模块以增强网络对长距离像素点之间的建模能力，从而在保证网络搜索能力的基础上提升模型的执行效率。实验结果表明：所提改进模型在降噪效果和执行效率两个方面显著优于原DIP模型，在降噪效果方面也超过了主流有监督降噪模型。Abstract: Given that the performance of the Deep Image Prior (DIP) denoising model highly depends on the search space determined by the target image, a new improved denoising model called RS-DIP (Relatively clean image Space-based DIP) is proposed by comprehensively improving its network input, backbone network, and loss function.Initially, two state-of-the-art supervised denoising models are employed to preprocess two noisy images from the same scene, which are referred to as relatively clean images. Furthermore, these two relatively clean images are combined as the network input using a random sampling fusion method. At the same time, the noisy images are replaced with two relatively clean images, which serve as dual-target images. This strategy narrows the search space, allowing exploration of potential images that closely resemble the ground-truth image. Finally, the multi-scale U-shaped backbone network in the original DIP model is simplified to a single scale. Additionally, the inclusion of Transformer modules enhances the network’s ability to effectively model distant pixels. This augmentation bolsters the model’s performance while preserving the network’s search capability. Experimental results demonstrate that the proposed denoising model exhibits significant advantages over the original DIP model in terms of both denoising effectiveness and execution efficiency. Moreover, regarding denoising effectiveness, it surpasses mainstream supervised denoising models.
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表 1 不同降噪模型组合对降噪性能的影响(dB)
算法组合 Restormer+
DnCNNRestormer+
FFDNetRestormer+
DAGLDAGL+
DnCNNDAGL+
FFDNetDAGL+
SwinIRRestormer+
SwinIR1 36.33 36.29 35.79 36.19 35.77 36.08 36.44 2 36.74 36.64 36.06 36.43 36.14 36.48 36.84 3 35.25 34.78 34.78 34.78 35.57 35.28 35.03 4 31.33 31.49 31.48 31.55 31.56 31.62 31.58 5 38.41 37.00 37.57 38.21 36.86 37.30 37.31 6 38.51 40.11 38.53 38.40 39.72 39.82 40.23 7 30.19 30.27 30.02 29.58 29.78 29.77 30.32 8 34.52 34.69 34.17 33.78 33.98 34.03 34.70 9 35.83 35.41 34.79 34.53 34.81 34.80 35.50 10 36.65 36.38 36.04 36.14 35.85 35.94 36.48 平均值 35.27 35.31 34.98 34.96 35.00 35.11 35.44 
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表 2 随机采样操作对模型降噪性能的影响比较(dB)
对比算法 RS-DIP-1 RS-DIP 1 36.42 36.44 2 36.90 36.84 3 34.93 35.03 4 31.59 31.58 5 37.18 37.31 6 40.14 40.23 7 30.30 30.32 8 34.70 34.70 9 35.49 35.50 10 36.36 36.48 平均值 35.40 35.44
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表 3 简化骨干网络对模型降噪性能的影响(dB)
对比算法 RS-DIP-2 RS-DIP 1 36.22 36.44 2 36.79 36.84 3 35.07 35.03 4 31.48 31.58 5 36.83 37.31 6 40.21 40.23 7 30.31 30.32 8 34.54 34.70 9 35.43 35.50 10 36.20 36.48 平均值 35.31 35.44
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表 4 各对比方法在各真实噪声数据集上所获得的PSNR值比较(dB)
数据集 PolyU NIND SIDD BM3D 33.90 32.87 38.00 DnCNN 33.28 31.33 32.84 FFDNet 34.25 32.72 37.54 DAGL 33.44 32.10 42.47 Restormer* 33.47 35.20 44.17 SwinIR* 34.45 33.84 37.24 DIP 34.43 33.79 39.42 RS-DIP 35.44 35.46 44.60 *表示该模型被用于处理噪声图像，在NIND和PolyU数据集上分别使用Restormer和SwinIR作为预处理算法处理不同的噪声图像，而在SIDD数据集上仅使用Restormer作为预处理算法处理两张噪声图像。
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