have achieved noticeable improvement with the development of convolutional neural network (CNN). Although most of image restoration methods work well on synthetically degraded images[21, 52, 9, 22], they show insufficient performance on the real degradations.
Regarding the denoising methods, the networks trained with synthetic-noise (SN) does not work well for the real-world images because of the discrepancy in the distribution of SN and real-noise (RN). Specifically, CNNs [48, 49, 50]
trained with Gaussian noise does not work well for the real-world images because the CNNs are overfitted to the Gaussian distribution. The problem of overfitting can also be seen from a toy regression example in Fig.1. As shown in Fig. 1(a), the severely overfitted regression method (‘w/o Regularizer’) shows worse performance than a regularized method (‘w/ Regularizer’) on the synthetic test data. Moreover, it can be seen in Fig. 1(b) that the generalization ability is much worse when the training and test domains are different.
To better address the problem due to the different data distribution between training and test sets, two kinds of approaches have been developed: (1) obtaining the pairs of RN image and corresponding near-noise-free image [34, 38, 5, 2, 43], and (2) finding more realistic noise model [17, 7].
The RN datasets enable the quantitative comparison of denoising performance on real-world images and also provide the training sets for learning-based methods. The CNNs trained with RN datasets robustly work on the real-world images, because domains of training and test set almost coincide. However, acquiring the pairs of RN images needs specialized knowledge, and the amount of provided datasets would not be enough for training a deeper CNN [46, 44]. Furthermore, learning-based methods can be easily overfitted to a specific camera device (dataset), which cannot cover all the devices that have different characteristics such as gamma correction, color correction, and other in-camera pipelines.
For a finding more realistic noise model, CBDNet  synthesized near-RN images by considering realistic noise models and simulating the in-camera pipeline. It generates enough dataset that simulates more than 200 camera response functions. The CBDNet shows excellent performance on RN images even though the CNN is trained with the SN. Furthermore, they showed that additional training with RN dataset improves performance. Although realistic noise modeling indeed reduces the domain discrepancy between SN and RN, there still remains a domain discrepancy to be handled. Moreover, CNN can be overfitted to a certain noise model that is actually not a ‘real’ noise.
From these observations, we propose a novel denoiser that is well generalized to the various RN from camera devices by employing an adaptive instance normalization (AIN) [41, 19, 28, 37]. In recent CNN based methods for restoring the synthetic degradations [29, 52, 22], regularization methods have not been exploited due to the small performance gain (even degrading performance). This indicates that a CNN is overfitted to the training data to get the best performance when domains of training and test set coincide .
On the other hand, the denoiser trained with SN needs regularization, for applying it to the RN denoising. As shown in the example of Fig. 1 (a) and (b) with ‘w/ Regularizer’, the network needs to be generalized through the regularization. In this respect, we propose a well-regularized denoiser by adopting the AIN as a regularization method. Specifically, the affine transform parameters for the normalization of features are generated from the pixel-wise noise level. Then, the transform parameters adaptively scale and shift the feature maps according to the noise characteristics, which results in the generalization of the CNN.
Furthermore, we propose a transfer learning scheme from SN to RN denoising network to reduce the domain discrepancy between the synthetic and the real. As mentioned above, the RN dataset would not be sufficient to train a CNN, which can also be easily overfitted to a certain RN dataset. Hence, we devise a transfer learning scheme that learns the general and invariant information of denoising from the SN domain and then transfer-learns the domain-specific information from the information of RN. As can be seen in Fig. 1(c), we believe that the SN denoiser can be adapted to an RN-denoiser by re-transforming normalized features. Specifically, the parameters of AIN are updated using an RN dataset. The proposed scheme based on transfer learning can be applied to any dataset that has a small number of labeled data. That is, a CNN trained with the SN is easily transferred to work for the RN removal, without the need for training the whole network with the RN.
The contribution of this work can be summarized as follows:
We propose a novel well-generalized denoiser based on the AIN, which enables the CNN to work for various noise from many camera devices.
We introduce a transfer learning for the denoising scheme, which learns the domain-invariant information from SN data and updates affine transform parameters of AIN for the different-domain data.
The proposed method achieves state-of-the-art performance on the SN and RN images.
2 Related Works
The statistics of RN in standard RGB (sRGB) images depend on the properties of camera sensors and in-camera pipelines. Specifically, shot noise and readout noise are generated from the sensor, and the statistics of generated noise are changed according to the in-camera pipeline such as demosaicing, gamma correction, in-camera denoiser, white balancing, color correction, etc . There have been several works to approximate the RN model, including Gaussian-Poisson [14, 30]
, heteroscedastic Gaussian
, Gaussian Mixture Model (GMM), and deep leaning based methods [10, 1]. Considering the camera pipeline, CBDNet  and Unprocessing  also considered realistic noise models. Specifically, they obtained near-RN images by adding the heteroscedastic Gaussian noise to the pseudo-raw images and feeding them to the camera pipeline. These methods can simulate more than 200 camera response functions, and thus generate noisy images having different characteristics. Moreover, CBDNet is alternately trained with the RN and SN to overcome overfitting to the noise model. We think the alternate training scheme would incur training instability due to different data distributions, and also cannot train quite different RN effectively. Thus, we introduce a new transfer learning scheme that can simply but effectively adapt SN denoiser to other RN ones by re-transforming the normalized feature map.
We aim to train a robust RN denoiser, which reduces the discrepancy between the distributions of training and test sets, by proposing a novel denoiser and transfer learning. Precisely, we propose denoising architecture using the AIN, which can be well generalized to RN images. Also, we introduce a transfer learning scheme to reduce the remaining data discrepancy, which consists of two stages: (1) training a denoiser with SN dataset and (2) transfer learning with RN dataset , where and are noisy image and noise-free image respectively, and the subscript is for SN and for RN. We use the noise model from CBDNet for generating from with the noise level of where denotes SN image. After training SN denoiser with , RN denoiser is trained with RN image and near noise-free image . In the transfer learning stage, domain-specific parameters are only updated to effectively preserve learned knowledge from SN data.
3.1 Adaptive Instance Normalization Denoising Network
We present a novel AIN denoising network (AINDNet), where the same architecture is employed both for SN and RN denoiser. We compose AINDNet with a noise level estimator and a reconstruction network, which is presented in Fig. 2. The noise level estimator takes a noisy image y as an input and generates the estimated noise level map where denotes a training parameter of estimator. The reconstruction network takes (y) and y as input and generates denoised image where denotes a training parameter of reconstruction network. The reconstruction network is U-Net based architecture with AIN Residual blocks (AIN-ResBlocks).
Noise Level Estimator
Estimating the noise level would not be an easy task due to the complex noise model and in-camera pipeline. In our experiment, we find that previous simple noise level estimators [17, 7], which consist of five convolutions, could not accurately estimate the noise level. The main reason is that the previous estimators have a small receptive field so that it could not fully capture complex noise information. From this observation, we design a new noise level estimator with a larger receptive field by employing down/up-sampling and multi-scale estimations. Specifically, estimator produces down-scaled estimation map and original-sized estimation map . Then, these two outputs are weight averaged to feed reconstruction network:
where , ,
denotes the height and width of the image, and the linear interpolation respectively.is empirically determined to 0.8. From the weight average of multi-scale estimates, we can achieve region-wisely smoothed , which follows general the characteristic of RN.
Adaptive Instance Normalization
The proposed AIN-ResBlock plays two crucial roles in the proposed denoising scheme. One is regularizing the network not to be overfitted to SN images, and the other is adapting SN denoiser to RN denoiser. For this, we build AIN-ResBlcok with two convolutions and two AIN modules, which is presented in Fig. 3. The AIN module affine transforms normalized feature map of convolution by taking a conditional input where denotes the spatial size of feature map at each scale , and is the number of channels. Specifically, the AIN module produces affine transform parameters such as scale () and shift () for each pixel. Thus, every feature map is channel-wisely normalized and pixel-wisely affine transformed according to the noise level. The update process of feature map in AIN module at site (, , ) is formally represented as
where the variables with superscript * are generated from , and and
denote the mean and standard deviation ofrespectively, in channel . Precisely,
where denotes the stability parameter, which prevents divide-by-zero in eq. (2), and we set in our implementation. Note that and can be generated pixel-wisely and thus the proposed method can process spatially variant noisy images adaptively.
3.2 Transfer Learning
We propose transfer learning scheme to leverage to accelerate the training of RN denoiser with that has a limited number of elements (RN pairs). We expect that SN denoiser learns general and invariant feature representations and RN-denoiser learns noise characteristics that cannot be fully modeled from SN data. The proposed transfer learning scheme can achieve these two merits by adapting SN denoiser to RN denoiser. For this, we focus on normalization parameter to handle different data distribution, which is inspired from other style transfer and classification tasks [41, 19, 37]
. In these methods, transforming normalization parameters can transfer different style domain, and different domain classifications can be handled by switching the batch normalization parameters. From these observations, we try to adapt different domain denoisers by transfer-learning the normalization parameters assuming that data discrepancy betweenand can be adapted by re-transforming the normalized feature maps.
Specifically, AIN parameters of SN denoiser can be adapted pixel-wisely with conditional . Thus, AIN modules and noise level estimator are transfer-learned with RN data. Although the objective function of noise level cannot be present in , noise level estimator can be trained with the reconstruction loss. We consider that last convolution plays a crucial role reconstructing feature maps to RGB image, hence last convolution is also updated. The overall proposed transfer learning scheme is presented in Fig. 4.
Since the proposed transfer learning scheme only updates the parts of well generalized denoiser, it can be converged with faster speed and get better performance with very few number of elements from than training from scratch. Moreover, the proposed scheme effectively copes with multiple models, which are inevitably required due to severely different noise statics, saving lots of memory by switching specific parameters.
For training SN denoiser, we exploit multi-scale asymmetric loss as an estimation loss where asymmetric loss is introduced from CBDNet  to prevent under estimation. Formally, multi-scale asymmetric loss is defined as,
where , , and
denote element-wise operations such as indicator function, multiplication, and power respectively. Hyperparametersare empirically determined as . is achieved from average pooling .
Then, the proposed SN denoiser is jointly trained with estimation loss and reconstruction loss as,
where denotes the SN denoiser training parameter including noise level estimator and reconstruction network. denotes the weight term of noise level estimator and is empirically determined to 0.05.
For the RN denoiser, it is only trained with reconstruction loss:
where denotes the RN denoiser training parameter that is transferred from . Previously stated parameter such as AIN modules, estimator, and last convolution are only updated, and other parameters are fixed when training the RN denoiser. We use Adam optimizer for both SN denoiser and RN denoiser.
We present the results of AWGN and RN images by training a Gaussian denoiser and RN denoiser.
4.1 Experimental Setup
For the Gaussian denoiser, the training images are obtained from DIV2K  and BSD400 , and noisy image is generated by AWGN model. For the RN denoiser, we train a denoiser with two step: training an SN denoiser and training an RN denoiser by transfer learning. We achieve pairs of SN images and noise-free images from Waterloo dataset  with heteroscedastic Gaussian noise model and simulating in-camera pipelines. The RN denoiser, which is transferred from SN denoiser, is trained with SIDD training set . All the training images are cropped into patches of size .
RNI15  is composed of 15 real-world noisy images. Unfortunately, the ground-truth clean images are unavailable, therefore we only present qualitative results.
DND  provides 50 noisy images that are captured by mirrorless cameras. Since we cannot access near noise-free counterparts, the objective results (PSNR/SSIM) can be achieved by submitting the denoised images to DND site.
SIDD  is obtained from smartphone cameras. It provides 320 pairs of noisy images and corresponding near noise-free ones for the learning based methods where the captured scenes are mostly static. Furthermore, it provides 1280 patches for validation that has similar scenes with training set. The quantitative results (PSNR/SSIM) can be achieved by uploading the denoised image to SIDD site.
4.2 Comparison with state-of-the-arts
Noise Level Estimation
We evaluate an accuracy of noise level estimator on exploited noise model images. We compare the proposed noise level estimator with fully convolutional network (FCN) that are widely used [17, 7]. In order to evaluate the accuracy of estimator itself, each estimator is trained with regression. The employed quantitative measurements are mean absolute error (MAE) and standard deviation (STD) of the error. We report the accuracy of each estimator in Table 1 where the input images are simultaneously corrupted with signal dependent noise level and signal independent noise level . We can find that proposed estimator gets more accurate results than previous estimator with a similar number of parameters. The results of more various noise levels will be presented in supplementary file. Furthermore, we will present the denoising performance when combined with reconstruction network, in ablation study.
We compare proposed denoiser on the noisy grayscale images that are corrupted by AWGN. For this, we train Gaussian denoiser in a single network that learns noise level in [0,60]. The comparisons between the proposed method and other methods are presented in Table 2. We can see that the proposed denoiser achieves the best performance on Set12 where composition of Set12 is independent from training sets. On the other hand, the proposed method gets second best performance on BSD68 that consists of similar objects in BSD400 (training set). We think these results present robust generalization ability of the proposed denoising architecture for training set.
Real Noise Denoising
We also investigate proposed denoiser and transfer learning scheme on RN datasets. Processing RN image is considered very practical, but difficult, because the noises are signal dependent, spatially variant, and visualized diversely according to different in-camera pipelines. Thus, we think RN denoising is an appropriate task for showing the generalization ability of the proposed denoiser and the effects of the proposed transfer learning.
For the precise comparison, we train four different denoisers according to training sets and learning methods:
AINDNet(S): AINDNet is trained with SN images, which is proposed SN denoiser.
AINDNet(R): AINDNet is trained with RN images.
AINDNet+RT: All the parameters from AINDNet(S) are re-trained with RN images, which is common transfer learning scheme.
AINDNet+TF: AIN parameters from AINDNet(S) are updated with RN images, which is proposed RN denoiser.
Moreover, we present the geometric ensemble results denoting super script in order to maximize potential performance of the proposed methods.
Meanwhile, there have been a challenge on real image denoising  where the SIDD is used. Our method shows lower performance than the top-ranked ones in the challenge, but it needs to be noted that the number of parameters of our network is much smaller than those in the challenge. For example, DHDN  and DIDN  that appeared in the challenge require about 160 M and 190 M training parameters respectively which are about 12 - 15 times larger than ours. Moreover, challenge methods have been slightly overfitted to SIDD where the winning denoiser  gets comparably lower performance (38.78 dB) on DND than our method. Therefore, we would not directly compare the proposed method with challenge methods.
The comparisons, including internal comparisons, are presented in Table 3 and 4. We can find that proposed methods get the best performance on DND and SIDD benchmarks. Specifically, the proposed AINDNet(S) achieves the best performance on DND benchmark, which is impressive performance that outperforms RN trained denoisers. Moreover, AINDNet(S) gets 1.5 dB and 2.4 dB gains from CBDNet on DND and SIDD respectively where employed noise models are the same. These results indicate that the proposed denoiser is not overfitted to noise model and can be well generalized to RN images. However, AINDNet(S) has inferior performance than AINDNet(R) on SIDD with big margin. The main reason is that AINDNet(R) is solely trained with SIDD training images where test set consists of similar scenes and objects in training set. In other words, AINDNet(R) can be slightly overfitted to SIDD benchmark and this phenomenon can be seen from insufficient performance on DND.
In contrast, AINDNet+RT and AINDNet+TF get satisfying performance on both DND and SIDD. Concretely, AINDNet+RT and AINDNet+TF have better performance than others, including AINDNet(R) on SIDD, which indicates that pre-training the SN images results in better performance. AINDNet+TF more likely preserves priorly learned knowledges from SN data than AINDNet+RT, so AINDNet+TF achieves the best overall performance among compared methods.
We present visualized comparisons on SIDD and RNI15 in Figs. 6 and 6, which show that proposed methods remove noises robustly while preserving the edges. Thus, characters in output images are more apparent than in other methods’ results. Furthermore, we also present visual enhancement in Fig. 7 when the proposed transfer learning scheme is applied. Since RN denoiser transfer-learns characteristics of RN, AINDNET+TF successfully removes unusual noise that cannot be removed with AINDNET(S). Moreover, RN denoiser learns the properties of JPEG compression artifacts that is not priorly learned in SN denoiser, so it can also successfully reduces compression artifacts. We will also present other visualized comparisons in supplementary file.
Effect of Transfer Learning with Limited RN Pairs
We investigate the relation between denoising performance and the amount of RN image pairs in , because we consider that preparation of is quite difficult and the number of elements can also be limited. For this, we train each network with constrained image pairs from one to all (320) from SIDD . The average PSNR of each denoiser is presented in Table 5. It can be seen that transfer learning schemes can infer significant performance with the small number of real training images. It is notable that AINDNet+TF trained with 32 pairs of real data achieves better performance than RIDNet that exploits all. Thus, we can conclude that the transfer learning with SN denoiser dramatically accelerate the performance with a small number of labeled data from other domain.
Architecture of Denoiser
We demonstrate the effectiveness of noise level estimator and reconstruction network for training with . We present performance of noise level estimators combined with reconstruction network in Table 6 with different objective function. Remember that can generate smoothed outputs, so is excluded when using . We find that state-of-the-art training scheme (FCN + + ) infers inferior performance than proposed training scheme (Ours + ). Moreover, the proposed training scheme also surpasses internal variation (Ours + + ).
We further investigate the effectiveness of reconstruction network. For this, we select an adaptive Gaussian denoiser that can process spatially variant noise map by feeding gated-residual block (Gated-ResBlock). Since it has not reported the performance on RN dataset, we train SN-denoiser by replacing AIN-ResBlock to Gated-Resblock where other settings are same as AINDNet. Table 6 shows that the proposed AIN-ResBlock shows better performance on RN datasets. Thus, we believe that the AIN-ResBlock is an appropriate architecture for the generalization.
In this paper, we have presented a novel denoiser and transfer learning scheme of RN denoising. The proposed denoiser employs an AIN to regularize the network and also to prevent the network from overfitting to SN. The transfer learning mainly updates the AIN module using RN data to adjust data distribution. From the experimental results, we could find that the proposed denoising scheme can be well generalized to RN even if it is trained with SN. Moreover, the transfer learning scheme can effectively adapt an SN denoiser to an RN denoiser, with very few additional training with real noise pairs. We will make our code publicly open for further research and comparison.
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