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Learning Deep Image Priors for Blind Image Denoising

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Learning Deep Image Priors for Blind Image Denoising Learning Deep Image Priors for Blind Image Denoising Xianxu Hou 1 Hongming Luo 1 Jingxin Liu 1 Bolei Xu 1 Ke Sun 2 Yuanhao Gong 1 Bozhi Liu 1 Guoping Qiu 1,3 1 College of Information Engineering and Guangdong Key Lab for Intelligent Information Processing, Shenzhen University, China 2 School of Computer Science, The University of Nottingham Ningbo China 3 School of Computer Science, The University of Nottingham, UK Abstract ods comes from the effective image prior modeling over the input images [5, 14, 20]. State of the art model-based meth- Image denoising is the process of removing noise from ods such as BM3D [12] and WNNM [20] can be further noisy images, which is an image domain transferring task, extended to remove unknown noises. However, there are a i.e., from a single or several noise level domains to a photo- few drawbacks of these methods. First, these methods usu- realistic domain. In this paper, we propose an effective im- ally involve a complex and time-consuming optimization age denoising method by learning two image priors from process in the testing stage. Second, the image priors em- the perspective of domain alignment. We tackle the domain ployed in most of these approaches are hand-crafted, such alignment on two levels. 1) the feature-level prior is to learn as nonlocal self-similarity and gradients, which are mainly domain-invariant features for corrupted images with differ- based on the internal information of the input image without ent level noise; 2) the pixel-level prior is used to push the any external information. denoised images to the natural image manifold. The two In parallel, there is another type of denoising methods image priors are based on H-divergence theory and imple- based on discriminative learning. They aim to train a deep mented by learning classifiers in adversarial training man- denoising network with paired training datasets (noisy and ners. We evaluate our approach on multiple datasets. The clear images) and learn the underlying noise model implic- results demonstrate the effectiveness of our approach for ro- itly to achieve fast inference [6, 57, 58, 29], among which bust image denoising on both synthetic and real-world noisy DnCNN [56] and FFDNet [58] have obtained remarkable images. Furthermore, we show that the feature-level prior results. However existing discriminative learning methods is capable of alleviating the discrepancy between different are usually designed to a specific noise level with limited level noise. It can be used to improve the blind denoising flexibility. Though DnCNN-B [56] can be trained for dif- performance in terms of distortion measures (PSNR and ferent noise levels for blind image denoising, it still cannot SSIM), while pixel-level prior can effectively improve the generalize well to real-world noisy images. FFDNet [58] perceptual quality to ensure the realistic outputs, which is still requires a tunable noise level map as the input to tackle further validated by subjective evaluation. various noise levels. Therefore, it is of great interest to de- velop general image priors which can help handle image denoising with a wide range of noise levels and generalize 1. Introduction well for real-world noisy images. To this end, we propose a new image denoising model, Image denoising is a fundamental problem in low-level referred to Deep Image Prior Network (DIPNet), based on vision as well as an important pre-processing step for many data-driven image priors. In particular, we consider image other image restoration problems [57, 23]. It aims at recov- denoising as a domain transferring problem, i.e., from noise ering a noise-free image x from its noisy observation(s) y domain to photo-realistic domain. Inspired by this, we pro- by following the degradation model y = x + v. As in many pose two image priors: 1) the feature-level prior which is previous literatures [5, 14, 57, 41, 58], v is usually assumed designed to help decrease domain discrepancy between cor- additive white Gaussian noise (AWGN) of standard devi- rupted images with different noise levels for robust image ation σ. Therefore, prior knowledge modeling on images denoising; 2) the pixel-level prior which is used to push plays an essential part in image denoising. the denoised image to photo-realistic domain for percep- The main success of the recent image denoising meth- tual improvement. In particular, we model both priors as discriminator networks, which are trained by an adversar- Discriminative Learning Based Methods try to model ial training strategy to minimize the H-divergence between image prior implicitly with paired (noisy and clear images) different image domains. training data. These models have achieved great success in The contribution of this work can be summarized as fol- image denoising by taking advantage of the recent devel- lows: opment of deep learning. Several approaches adopt either a plain Multilayer Perceptron (MLP) [6] or convolutional • We propose an effective deep residual model based neural networks (CNN) [29, 53, 1, 34, 57, 7] to learn a non- on data-driven image priors, namely DIPNet, for blind linear mapping from noisy images to photo-realistic ones. image denoising. Our method can achieve state of the It is worth to mention that remarkable results have been ob- art results for both synthetic and real-world noise re- tained by recent deep learning based models. DnCNN [56] moval. successfully trains a deep CNN model with batch normal- ization and residual learning to further boost denoising per- • We design two image priors based on adversarial train- formance. Moreover, DnCNN can be extended to handle ing. The feature-level prior is capable of alleviating the noisy images with different level noise. A generic image- discrepancy between different noise levels to improve to-image regression deep model (RBDN) [48] can be ef- the denoising performance, while pixel-level prior can fectively used for image denoising. A deep kernel predic- effectively improve the perceptual quality and produce tion network [42] is trained for denoising bursts of images photo-realistic results. taken from a handheld camera. GAN-CNN [8] proposes to use generative adversarial network (GAN) to estimate the • Compared with previous methods, our method sig- nificantly improves the generalizability when adapt- noise distribution for blind image denoising. Furthermore, ing from synthetic Gaussian denoising to real-world FFDNet [58] presents a fast and flexible denoising convo- noise removal. In particular, a single model trained for lutional neural network, which can handle a wide range of blind Gaussian noise removal can outperform compet- noise levels with a tunable noise level map. Recently deep ing methods designed specifically for real-world noise image prior [51] is also proposed for general image restora- removal. tion. However, most previous discriminative learning based methods have to learn multiple models for handling images 2. Related Work with different noise levels. It is still a challenging issue to develop a single discriminative model for general image de- 2.1. Image Denoising noising for both synthetic and real-world noises. Deep Learning on Image Transformation. Beyond A large number of image denoising methods have been image denoising, deep CNNs have been successfully ap- proposed over the recent years, and generally they can be plied to other image transformation tasks, where a model grouped into two major categories: model-based methods receives a certain input image and transforms it into the and discriminative learning based methods. desired output. These applications include image super- Model-based Methods are usually depended on human- resolution [13], downsampling[24], colorization [32], de- crafted image priors such as nonlocal self-similarity [14, 41, blurring [35], style transfer [30], semantic segmentation 5], sparsity [15] and gradients [47, 52]. Two of the classic [39], image synthesis [25, 19] etc. In addition, [4] analy- methods are BM3D [12] and WNNM [20], which are usu- ses the trade-off between the distortion and perception mea- ally used as the benchmark methods for image denoising. sures for image restoration algorithms. However their mod- In particular, BM3D uses an enhanced sparse representation els are designed to handle input images within a specific do- for denoising by grouping similar 2D image fragments into main, and cannot be directly used for blind image denoising 3D data arrays. WNNM proposes to use weighted nuclear with unknown noise level. In this work, we focus on using norm minimization for image denoising by exploiting im- a single model to effectively handle a wide range of noise age nonlocal self-similarity. These models can be also fur- levels with data-driven image priors and also investigate the ther extended to handle blind denoising problem with vari- perception-distortion trade-off in terms of image denoising. ous noise levels. In addition, a few approaches [38, 44, 60] are also proposed to directly address blind image denoising 3. Method problem by modeling image noise to assist corresponding denoising algorithms. However, these models are based on Our goal is to develop a model which can take the noisy human-crafted priors which are designed under limited ob- images to produce photo-realistic ones. The primary chal- servations. In this paper, instead of only using the internal lenges are twofold: first, our model should be flexible and information of the input images, we propose to automati- robust to process the same images corrupted with different cally learn image priors by making full use of external in- level noise; second, we must ensure that the denoised im- formation. ages are realistic and visually pleasing. To address these challenges, we propose to learn two image priors based on during forward propagation and reverses the gradient by H-divergence theory, considering that the corrupted images multiplying it with a negative constant during the backprop- with different level noise as well as clear images are in dif- agation.
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