NERDS: A General Framework to Train Camera Denoisers from Raw-RGB Noisy Image Pairs
Authors: Heewon Kim
Kyoung Mu Lee
Venue: ICLR 2023 poster (2023)

Abstract:   We aim to train accurate denoising networks for smartphone/digital cameras from single noisy images. Downscaling is commonly used as a practical denoiser for low-resolution images. Based on this processing, we found that the pixel variance of the natural images is more robust to downscaling than the pixel variance of the camera noises. Intuitively, downscaling easily removes high-frequency noises than natural textures. To utilize this property, we can adopt noisy/clean image synthesis at low-resolution to train camera denoisers. On this basis, we propose a new solution pipeline -- NERDS that estimates camera noises and synthesizes noisy-clean image pairs from only noisy images.  In particular, it first models the noise in raw-sensor images as a Poisson-Gaussian distribution, then estimates the noise parameters using the difference of pixel variances by downscaling. We formulate the noise estimation as a gradient-descent-based optimization problem through a reparametrization trick. We further introduce a new Image Signal Processor (ISP) estimation method that enables denoiser training in a human-readable RGB space by transforming the synthetic raw images to the style of a given RGB noisy image. The noise and ISP estimations utilize rich augmentation to synthesize image pairs for denoiser training. Experiments show that our NERDS can accurately train CNN-based denoisers (e.g., DnCNN, ResNet-style network) outperforming previous noise-synthesis-based and self-supervision-based denoisers in real datasets.

Published as a conference paper at ICLR 2023
NERDS: A GENERAL FRAMEWORK TO TRAIN CAM- ERA DENOISERS FROM RAW-RGB NOISY IMAGE PARIS
Heewon Kim
Kyoung Mu Lee
1 The Global School of Media, College of IT, Soongsil University, Korea
2 ASRI, Department of ECE, Seoul National University, Korea
3 IPAI, Seoul National University, Korea
wwwkmesss sumessu.aee
kyoungmuesmt snu. ac.kr
ABSTRACT
We aim to train accurate denoising networks for smartphone/digital cameras from raw-RGB noisy image pairs. Downscaling commonly used a practical de- noiser for low-resolution images. Based on this processing, we found that the pixel variance of natural images is more robust to downscaling than the pixel variance of camera noise. Intuitively, downscaling removes ily than natural textures. To utilize this property, we can adopt noisy/clean image synthesis at low-resolution to train camera denoisers. On this basis, we propose a new solution pipeline - NERDS that estimates camera noise and synthesizes noisy-clean image pairs from only noisy images. In particular, it first models the noise in raw-sensor images as parameters using the difference of pixel variances by downscaling. We formulate the noise estimation as a gradient-descent based optimization problem through a reparametrization trick. We further introduce new Image Signal Processor (ISP) estimation method that enables denoiser training in a human-readable RGB space by transforming the downscaled raw images the style of a given RGB noisy im- age. The noise and ISP estimations utilize rich augmentation to synthesize image pairs for denoiser training. Experiments show that NERDS can accurately train CNN-based denoisers (e.g., DnCNN, ResNet-style network) outperforming previ- ous noise-synthesis-base and self-supervision-bee denoisers real datasets.
noise more eas-
distributions, then estimates noise
INTRODUCTION
Image denoising is conventional machine learning problem restoring original colors and patterns from noisy images. Deep-learning-bast based approaches have achieved breakthroughs in recent decades due to the power of neural networks. Early works (55; 38; 45) have successfully removed additive white Gaussian noise (AWGN), which allows network training under supervision by synthesizing noisy-clean image pairs.
Nevertheless, denoising images captured by smartphone/digital cameras poses an obstacle, as is difficult to obtain clean images for noisy images with pixel-level alignment. Several works (2; 7) constructed datasets with the noisy-clean pairs for real-world images. Using these pairs (Fig- ure ((a)), many supervisee--earring--aas benchmarks from the datasets. However, constructing such datasets requires tightly controlled cap- turing environments, complicated post-processing, and massive human labor.
denoisers (51; 28; 52; 24; 15) restore crisp images on
To overcome the drawback of plain supervised learning, two major types of research have been studied. The first line of works generates realistic noisy images from clean images to utilize su- pervised denoiser training visualized in Figure 1(b). Several approaches (14; 11; 23; 26) adopt generative models using unpaired noisy-clean images based on GAN (20), but they achieve limited accuracy on real noise. Some other works synthesize realistic noise using existing noisy-clean image pairs (53; 1) or metadata for real cameras (6; 22), but they are limited in generalization for unseen noise. The second category aims learn denoisers without clean images. The first work (34) in this category proposed the learning framework using multiple noisy images. After that, many self- supervised-feaming approaches (5; 31; 10) use single noisy images (Figure ((c)), which enable easy

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Clean
Neisy
Downscaling
Noise Synthesis
Noise Synthesis
Clean
Clean
Nojay
Void
Clean
Input
Target
Input
Target
Input
Target
Input
Target
(a) Traditional
(b) Noise Flow
(c) Vois (N2V)
(d) NERDS (Ours)
Figure I: Different training schemes for CNN-based camera denoisers. Traditionally, training denoisers requires pairs of noisy and clean images. However, clean target images are difficult to obtain from smart- phone/digital cameras. (b) Noise Flow (1) generates realistic noise from clean images by learning real noise distributions using existing pairs of real images, (c) N2V (31) enables practical training from noisy images without clean targets but requires custom network architectures. (d) Our NERDS generates pseudo-noiss noisy and pseudo-clean image pairs low-resolution by utilizing image downscaling as a general denoiser and noise estimation through gradient-descent
optimization.
data collection and denoiser adaptation to the test noise. However, they are still limited in real-world applications due to the requirements of custom network architectures and strong statistical noise assumptions.
To address the above limitations on camera denoiser training, we propose a new solution pipeline, namely Noise Estimation for RGB Denoising & Synthesis (NERDS), that generates noisy-clean image pairs from raw-RGB noisy image pairs. The pipeline composes three ISP estimation, and denoiser training. We found that the pixel variance of natural clean images is robust to image downscaling, which is a widely-used denoiser for low-resolution images (41). The noise estimation adopts Poisson-Gaussia noise model for raw images from sensors and optimizes its noise parameters by the pixel variances of downscaled images. The downscaled images and the estimated noise parameters enable generating pseudo-noisy and pseudo-clean image pairs low- resolution (Figure (d)). Training denoisers on human-readable RGB images from real cameras has another issue: the conversion from the raw images to the RGB images black box. Our ISP estimation enables noise synthesis on the RGB space by learning RAW2RGB conversion using raw-RGB noisy image pairs'. Our denoiser training can utilize rich data augmentation based on estimated noise parameters and ISPs. Specifically, we introduce two techniques for this framework. First, reparametrization trick allows estimating noise parameters through gradient-descent-ie optimizer. Second, technique for style disentanglement from raw-RGB noisy image pairs. We summarize our contributions as follows:
estimation,
To the best of our knowledge, this is the first work to synthesize noisy-clean RGB image pairs low-resolution for accurate camera denoiser training from raw-RGB noisy image pairs.
We formulate noise estimation for Roisson-Gaussiae noise as an optimization problem, and novel reparameterization trick allows to estimate accurate noise parameters through gradient-descent.
We propose neural network that estimates the RAW2RGB conversions (or ISPs) used for given raw-RGB noisy image pairs. The ISP estimation generates realistic noisy-clean RGB image pairs from raw images.
Our frameworks can train general CNN-based denoisers (e.g. DrCNN, ResNet-style net- work) accurately for given test noisy images by performing noise synthesis using them.
Major camera manufacturers (e.g., Samsung, Apple, Xiaomi, Cannon, and Sony) provide raw and RGB
image pairs on their devices.
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RELATED WORK
2.1 BLIND IMAGE DENOISING
Traditional methods Classical methods usually denoise noisy images without training data using wavelet (17), filtering (8) including BM3D (16), optimization (18; 37; 21), and effective prior (57).
However, they perform limited accuracy compared to the recent deep-learning-
approaches.
Supervised-leerning-beer methods SIDD (2) and NIND (7) captured real noisy-clean image pairs to enable supervised-leaninng for real camera denoisers. However, the capturing procedure unacceptably expensive and cumbersome. We describe the literature on realistic noise synthesis methods for the supervised-lcanning. DnCNN (55) introduced a neural network to remove additive white Gaussian noise (AWGN) for the first time. However, Guo et al. (22) demonstrated the lim- itation of AWGN denoisers to -ignal-deeendett or ppatially-correlate characteristics of real-world images. To alleviate this problem, two lines of works have been researched. The first category uses generative models for noise synthesis with stable learning (14), various noise characteristics (26), knowledge distillation (47), conditional adversarial networks (23). Nevertheless, the scene statistics mismatched between clean and noisy datasets make it difficult to train accurate denoisers in practice.
noise, which are known as
suspervised-earniine (1I), and
The second category investigates camera noise modeling. CBDNet (22) transforms AWGN into realistic noise by using signal-dependent noise parameters and simulating in-camera ISP functions, such as gamma correction and demosaicing. UPI (6) converts RGB images to images and simulates noise using metadata of specific cameras. CyclelSP (53) trains neural networks for both RAW2RGB and RGB2RAW conversions large-scale datasets with specific ISP and noise set- tings, Noise Flow (1) synthesizes raw noisy images using normalizing flow and metadata. The work on (12) proposed a GAN-based framework for noise generation in raw images which adaptive to the camera. In (56), the authors claimed that the noise levels in metadata are inaccurate. Re- cently, the method in (30) models the RGB noise distribution using normalizing flow. SCUNet (54) proposed a practical noise model for a general- peneral-purpose purpose denoiser.
However, all the above methods require real noisy-clean image pairs, ISPs, metadata, or high-quality clean images which are not always available. More importantly, the noise synthesis using predeter- mined training datasets leads to poor denoising performances on unseen noise. contrast, the proposed method (NERDS) synthesizes realistic noisy-clean image pairs from only noisy images, which enables accurate denoiser training specialized in the test images.
Self-supervised-tearning bearnin--eann methods Noise2Noise (34) introduced framework that trains denoisers using noisy images only for the first time. Nois (31), Noise2Self (5), Noise2Same (48), and Neighbor2Neighbor (25) adopted advanced approaches which can train de- noisers with single images corrupted by the i.i.d noise. Noisier2Noise (40), NAC (49), and R2R (42) add additive noise to the given noisy images to make auxiliary training pairs by using prior knowl- edge (or assumptions) of the noise distribution. Notably, the blind-spot network (BSN) in (31) has been improved by efficient architectures with small receptive fields (32) and dilated convolu- tions (47). Using the advanced BSNs, FBI-D (10) adopts denoiser specialized in Poisson Gaussiin noise for rorl-world raw image denoising. AP-BSN (33) breaks the evatially-correlatee noise real- world RGB images by pixel-shuffle downsampling.
Although the above self-supervised-leaniii methods use practical training datasets (only noisy im- ages), they require custom network architectures strong noise assumptions. In contrast, NERDS performs noise synthesis that allows training general CNN-based denoisers based on the general noise modeling for smartphone/digital cameras.
2.2 NOISE ESTIMATION
Prior knowledge of noise distribution supports accurate restoration in most methods for image de- noising, but it is not generally available in practice. Noise estimation methods alleviate this problem, especially for the additive white Gaussian noise (AWGN) and the Poisson-Gaussian noise. Principal component analysis (PCA) based approaches (35; 44; 13) perform accurate AWGN estimation. For the Poisson-Gaussin noise model, most existing methods (3; 46) including (19; 36) adopt two-step
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approaches; estimating the local means and variances, then adopting maximum likelihood estimation (MLE) to fit the noise model. Foi et al. proposed the Poisson-Gause Gaussian noise model for the first time and noise estimation algorithm based wavelet decomposition. Liu et al. (36) adopted it- erative patch selection for the generalized source-dependent noise. Recently, PGE-Net (10) adopted neural networks for accurate and fast optimization based Generalized Anscombe Transformation (GAT) (4), In contrast, we proposed a noise estimation method for Poisson-Gaussian noise using natural scene statistics with sesffent-descee-bbssee optimization.
PRELIMINARY: NOISE MODELING
In a digital camera, an image sensor converts light into digital signal (or a raw image), and an image signal processor (ISP) converts it into human-readable RGB image. We regard that the noise of RGB images originates from the image sensor and transformed by the ISP. Thus, we model noise distribution of raw images and RAW2RGB conversion including ISPs.
Raw Image: Poisson-Gausiian (P-G) Noise A common noise model for raw images follows the Poisson-Gaussian distribution (19), defined
~ N(2 B3)
which is a heteroscedastic Gaussian where is the true signal, x is raw noisy image observed on real image sensors, and 31, 82 2 are signal-dependent and signal-independent noise parameters. well 81 and 83 in metadata. However, the noise parameters in metadata
Real cameras provide are often inaccurate (56). Section A.1.1 discusses the noise distribution of raw images and the noise parameters in metadata further.
RGB Image: Transformed Noise ISPs transform raw images into RGB images using nonlinear functions, such as demosaicing, white balancing, color correction, and tone mapping. The trans- formation changes noise distribution (e.g., breaking the i.i.d property in equation 1). Moreover, users can retouch image tones and colors, altering the noise distribution further. We define such RAW2RGB conversion (T) function of the image style (s),
T T(x;s). s):
(2)
where æ is a raw noisy image obtained from real image sensors and y is a RGB noisy image with custom tones and colors. The real noise datasets (2; 43) used simple and open-source ISP for the entire dataset without any image retouching processes. However, modern smartphones and dig- ital cameras use custom ISPs with hidden internal functions. They provide multiple image styles depending on scenes.
The following section provides a general framework to estimate P-G noise parameters (31, 32) and RAW2RGB conversion (T) with image style (s) using only noisy images (x, y).
4 PROPOSED METHOD
4.1 OVERVIEW
For given noisy pair of raw (x) and RGB (ÿ) images, the proposed method composes three steps to restore the RGB clean image without auxiliary training data. First, estimate noise parameters (3,, 32) from x (Section 4.3). Second, we learn the conversion from a to y while disentangling image style (8) (Section 4.4). Third, we synthesize diverse pairs of noisy-clean images to train arbitrary RGB denoisers with a general supervised-leaming framework (Section 4.5).
4.2 OBSERVATION
We first illustrate our observation on raw noisy images with downscaling. Specifically, we investi- gate the statistical characteristics variances of 256x256 images in the validation set of SIDD (2). To evaluate their noise levels, we downscale all raw images for each color channel with the scaling factor of 2, and rank these images according to the differences of pixel variances before and after
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High
0.025
Diffrrnce of Pixel Variances
0.02
Medium
0.015
0.01
Low
0.005
250
500
750
1000
1250
Image Incex
Figure The ranked curve for the difference pixel variances through downscaling on images from SIDD validation and the visualization of noise levels.
0.040
0.038
LI loss
Reparametrizattion Trick
Var
N No.. df
Var
Noiay
(Var: 0.029)
Clean
Differentiable noise
Sumpled noise
Downscaling
@ d+
Noose parameter)
T (Raw noisy image)
Figure Noise estimation from a raw noisy image with reparamettriation trick.
downscaling. As visualized in Figure 2, we show these values in a blue curve and separate them into three levels -low, medium, high. It is observed that the images with high differences are very noisy, while the images with low differences present clean textures. This phenomenon indicates that the true signal z is more robust to the pixel variances through downscaling than real noise. That why we propose the following method, which uses the difference of pixel variances estimate noise levels without clean images.
4.3 POISSON-GAUSSIAN NOISE ESTIMATION VIA A REPARAMETERIZATION TRICK
We aim estimate noise for æ without any information other than æ. For this, we find an additive noise (N) for the downscaled image (x by solving the following optimization problem,
min Var(#= Var(æ" N));
(3)
where N ~ (N0 Ff (82) is the Poisson-Gaussian noise discussed in equation and Var(-) denotes a function that outputs pixel variance of the input image. We can approximate 3, and 32 as noise parameters for æ, given that the difference of pixel variances through downscaling is correlated with noise levels, as discussed Section 4.2. Section A.1.2 analyzes the downscaling effect further. However, equation is difficult optimize due to the non-differentiable process of noise sampling. To alleviate this problem, we introduce a reparameterization trick that separates noise sampling into learnable parameters and sampling from normal distribution. Formally, reformulate equation? 3 as
min Var(x) - Var(x + PG(B, x²)) n)),
(4)

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Style Encoder (E)
Fullyroomeeeed leyars
Concntatoon aypexe
SA
[-3.7,
Demosalcing Ityers
Downsamp layers
Gloctel porking
Uppampking
(8ykpeemder)
RAW2RGB Converter (T)
(RGB nony
Figure estimation from a pair of raw-RGB noisy images with style disentanglement.
where PG x²)) (ffx* + 32)0.5 denotes a Poisson- Gaussian converter and (N 1) is additive noise with normal distribution. Given that the only learnable parameters in equation are 31 and 32, gradient-descee optimizer (e.g., ADAM (29)) easily finds the noise parameters. Figure visualizes the optimization problem used as noise estimation for æ.
Poisson-Gaussian
We are surprised that such a simple optimization problem estimates accurate noise parameters, while downscaled images can contain remaining noise or over-smoothing textures. This result is because the well-designed optimizer learns the characteristics of signal-dependent and signal-independent noise over the entire image content.
So we have discussed noise estimation and synthesis on raw images. For more practical applica- tions, the following section describes RGB noise synthesis by estimating RAW2RGB conversion.
4.4 ISP ESTIMATION WITH STYLE DISENTANGLEMENT
We aim to learn a RAW2RGB conversion (or an ISP) from & to y. However, a naive network training from to y can easily overfit to the image contents. Moreover, generate multiple styles of RGB images, training a neural network for each style is resource intensive. For this, we disentangle the styles from RGB images, allowing network learn multiple-style generation.
Specifically, our ISP estimation composes two networks, the style encoder (F) and the RAW2RGB converter (T) (Figure 4). E. identifies the image style a style parameter (s) from the raw noisy image (x) and the RGB noisy image (y) while 7' uses æ and 8 as input to generate an RGB image (g). Then, can recall the equation equation
(5) where E(x y) while training E' and T to minimize L1 differences between and y. E composes 6 residual blocks, global pooling, and a fully connected layer and T composes residual blocks and parameterized ISP functions (27). We use the residual blocks of two convolutional layers and one ReLU activation with 64 filters and 3x3 kernels. The training data can compose a single pair multiple pairs of raw-RGB noisy images. For accurate RAW2RGB conversion for downscaled images, which are unseen in training, we first adopt image scale augmentation (SA) that changes image resolution. SAA generates multiple training patches for a style (or patch) by randomly sampled scaling factors to prevent overfitting to a few training data. Formally, we redefine the style parameter (s) as follows,
T(x; E(x,y)),
s = E(SA(x, y)).
Second, design bottleneck structure that lowers the dimension of to avoid encoding of infor- mation about image contents. E reduces channels and resolutions of (s e 133xix1 in this paper). conditions T by channel attention and ISP parameter generation via fully connected layers.
4.5 DENOISER TRAINING WITH DATA AUGMENTATION
We synthesize clesn RGB image pairs at low-resolution to train general RGB denoisers. The pseudo-noisy/pseudo-dee images are the downscaled raw image (x:") with/without additive noise N transformed to RGB space using T. Formally, the denoiser (D) training minimizes the following objective function,
min [Tx D(T(xd +N:))) D

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where is the style parameter in equation 6. Our denoising framework allows rich augmentation for data synthesis. First, scaling and intensity augmentation (SIA) increases content diversity by changing the image resolution and pixel values from 0.5 to 1.5x. Second, we randomly scale noise parameters, 31 and 82, from 0.5x to 1,5x. This augmentation alleviates the noise estimation error in Section 4.3. Lastly, when multiple noisy images are given, we can augment the noise parameters and the style parameters across different images. Overall, the objective function of our denoiser training equation 7 becomes
min (T(SAA(""): - D(T(SIA(Z") + 8 D
(8)
where STA the scale and intensity augmentation, s' the augmented style parameter, and N' the additive noise with augmented noise parameters. At testing, the denoiser takes the RGB noisy image (y) as input. just like conventional CNN-based approaches.
5
EXPERIMENTS
5.1 IMPLEMENTATION DETAILS
Downscaling We downscale raw image æ to x by bicubic interpolation after asymmetric 2D Gaussian blurring with the kernel size 21 x 21. We randomly select the standard deviation of the Gaussian blur from 0.25 x ds to 0.75 x ds for each dimension, where ds denotes downscaling factor randomly selected in the range of [1.5, 2.5]. We use the same ISP estimation, and denoiser training unless otherwise specified.
for noise estimation,
Optimization We ADAM (29). 128x 128 image patches, and batch size of 64 for all ex- periments. Noise estimation adopts 2.5 x 10 iterations with the initial learning rate of 1 x 10-4 which becomes tenth part in every 5 103 iterations. We use the same initial learning rates for ISP estimation and denoiser training for x 109 iterations without the learning rate decay.
Denoiser We use two simple networks as denoiser to present the generalizability of the proposed training scheme. Specifically, NERDS+DnCNN uses DnCNN (55) and NERDS+D uses ResNet- style architecture, composing a global skip connection and 32 residual blocks. Each block has two convolutional layers and one ReLU activation with 64 filters and 3x3 kernels.
Dataset We use BSD68 (39) which consists of 68 gray-scale images to evaluate the performance of noise estimation. For real image denoising, we use raw noisy and RGB noisy images on SIDD (2), DND (43), and MIT-Adobe FiveK (9). SIDD (2) consists of training, validation, and benchmark datasets. We use 1,280 256x256 patches from 40 noisy images on the benchmark for denoiser training. the setting of extra images, we use 50 noisy images on the training dataset with low ISO levels of 100 and downscale the noisy images with the scaling factor from 1.1 to 1.2. For DND (43), we use 1,000 512 patches from 50 noisy images on the benchmark. We evaluate the denoising performances the benchmarks by submitting the results to the public websites for SIDD and DND. MIT-Adobe FiveK (9) consists of 5,000 raw images and paired RGB images retouched by photographers. Each RGB image has its own RAW2RGB conversion using Adobe Lightroom while the raw image contains real camera noise, We demonstrate an extreme scenario where only 5 noisy images are available for denoiser training.
5.2 RESULTS FOR NOISE ESTIMATION AND SYNTHESIS
We first validate our noise estimation on additive Dasson-Gaussian noise to images on BSD68 (39), and then visualize the noise synthesis at low-resolution from noisy images on MIT-Adobe FiveK (9).
Maisson-Gaussian noise estimation We demonstrate the effectiveness of our NERDS-raw on P-G noise estimation by comparing with Foi er al. (19), Liu et al. (36), and PGE-Net (10) which estimate P-G noise parameters from noisy images. Table 1 presents the average of the noise parameters estimated from each image on BSD68 with four different noise levels. In most cases, NERDS-ram -raw estimates the most accurate noise parameters.
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Table Performance comparison of Poisson-Gaussicn Gaussian noise estimation. The reported scores are average values of (81, 32) estimated from BSD68 with additive Poisson- Gaussian noise level of (8i. 82). Bold denotes the best result.
Noise level (9L, (Pa)
Foi et ($1_,02)
Liu et. ($1. 82)
PGB-Net
NERDS-raw (Ours) (b1, &2)
(81. 92)
(0. 100,00, 0.0200) (0.100. 0,0002)
(0.096. 0.042)
(0.072, 0.045)
(0.098, 0.0030)
(0.100, 0.0229)
(0.071. 0.044) (0.097.0.035)
(0.095. 0.0001)
(0.101. 0.0001)
(0.050, 0.0200)
(0.049. 0.031)
(0.040, 0.040)
(0.052, 0.0001)
(0.051, 0.0245)
(0.050. 0.0002)
00.051. 0.018)
(0.039. 0.034)
(0.051, 0.0001)
(0.052, 0.0002)
Real Image
NERDy
NERDS-clean Real Image
NERDS-noisy
NERDS-clean
Figure Noise synthesis results using NERDS. NERDS can synthesize noisy-clean RGB image pairs low- resolution from aw-RGB noisy image pairs. NERDS-noisy and NERDS-clean denote synthetic RGB images with and without noise. This example uses the downscaling factor of 2. (Zooming-in for the best view.)
Noisy-clean RGB image synthesis Figure shows noise synthesis results using NERDS. Each real image from MIT-Adobe FiveK has its own RAW2RGB conversion. NERDS-noisy contains realistic noise specialized to each real image, such as blue/dark noise in dark areas with none i.i.d characteristics. Figure visualizes more pseudo-noisy and pseudo-clean image pairs on SIDD/DND datasets. To the best of our knowledge, NERDS is the first work to generate the realistic noisy images and the paired clean images for the unknown RAW2RGB conversions (or ISPs) by using only raw-RGB noisy image pairs. Existing works for noise synthesis (6; 54; 53; 1; 30) require real noisy-clean image pairs, metadata, and ISPs. Thus, naive comparisons between NERDS and the existing methods are unfair, but we present them in Section 1.
5.3 COMPARISONS TO THE SOTA DENOISING METHODS
Results on benchmarks Given that NERDS trains denoisers without clean images, we com- pare our denoisers with the SotA denoising methods that do not use the pairs of real noisy- clean images. Specifically, we compare GAT+BM3D (16), N2V (31), AP-BSN (33), FBI-D (10), C2N+DIDN (26), and SCUNet (54) on SIDD and DND benchmarks. Table presents the effec- tiveness of the proposed denoisers, NERDS+DACNN and NERDS+D. C2N+DIDN (26) synthesizes realistic noise using unpaired noisy-clean images, while our NERDS+DOCNN outperforms it with the simpler denoiser (DIDN vs. DnCNN). SCUNet (54) synthesizes realistic noise using high- quality clean images and the predetermined noise models including specific noise levels and ISP pipelines. The other works require only noisy images via brsor-based filtering (16), self-supervised learning (31; 33; 47), and noise estimation (10). The works without clean images perform denois- ing in raw image space and received the results in RGB image space by submitting the denoised raw images to the public websites (2; 43). They often fail to remove noise in RGB image space, given that they assume strong noise characteristics such Poisson-Gaussian distributions and the i.i.d property that do not hold in the RGB image space. Section A.2 presents a generalization test for NERDS+D with latency analysis to denoise a test image from scratch. Section A.3.2 provides the comparisons with the noise synthesis methods which use the pairs of real noisy-clean images. Figure 10, 11, and 12 visualize more denoising results on SIDD, DND, and MIT-Adobe FiveK.
Ablation Study Our NERDS enables rich and effective augmentation for denoiser training. Ta- ble 3 demonstrates the ablation study on the data augmentation. The setting without image scale augmentation (Table 3(1) uses fixed downscaling factor of 2. Each component improves the
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Table 2: Performance comparison with SotA denoising methods. The reported scores PSNR (dB)SSIM RGB images from the SIDD and DND benchmarks, Bold denotes the best result. v denotes accessible types of images at training. Extra images denote images other than the noisy images of the benchmark.
Dataset
GAT+BM3D
N2V
AP
FBIDD
C2N+DIDN SCUNet
NERDS+DiCNN
NERDS+D
Clean images
images
Synthetic pains
SIDD
34.6110.879 82.85/0.847 36, 96/999993 88.07/0.942 35.35/09937 22.89/0.797
36.42/0.923 37.4000941 38.2880.949 38.21/0.941
DND
37.98/0.920 35.82/0.902 38.0910.937 38.98/0.945 37.28/0.924
39.34/0.950
Table Ablation study on data augmentation to train NERDS+D, PSNR (dB)/SSIM on RGB images from SIDD validation.
Augmentation
(2)
(3)
(4)
(5)
(6)
Image scale
V
V
Image intensity
Noise parameter
Style parameter
Extra images
SIDD
35.63/0.897 36.79/0.928 37.16/0.935 37.84/0.444 38.02/0.446 38.51/0.950
Real Image
CycleISP
NERDS (Ours) Real Imuge
SCUNet
NERDS-D (Ours)
Figure Qualitative comparisons of denoising results on MIT-Adobe FiveK (9). Pink arrows indicate re- maining noise of CycleISP and the over-smooth textures of SCUNet while our denoiser generates crisp images. (Zooming-a) in for the best view.)
restoration performances. In particular, noise parameter augmentation improves over than 0.6 dB, given that can alleviate noise estimation error. The usage of extra images enables high-quality clean image synthesis by downscaling images with low noise levels and small scaling factors.
Results on MIT-Above FiveK We evaluate CycleISP (53), SCUNet (54), and NERDS+D
touched images from MIT-Adobe FiveK (9), CycleISP (53) employs supervised learning on SIDD training dataset with additional synthetic data using the predetermined noise levels and ISP pipeline. Nevertheless, CycleISP fails to denoise the retouched image as visualized in Figure 6. SCUNet (54) trained high-quality images with practically designed additive noise, but SCUNet often gener- ates over-smooth textures. These results are due to different noise distributions between the training images and the test images. In contrast, NERDS+D removes severe real noise while maintaining image details. We report more qualitative results in Figure 12.
CONCLUSION
We present general framework to train denoisers from noisy images, called NERDS. The frame- work composes noise estimation, ISP estimation, and denoiser training. For noise synthesis, we estimate Poisson-Gaussian noise in raw images and ISP (or RAW2RGB conversion) for each RGB image. NERDS allows rich data augmentation for accurate denoiser training. Experimental results show the state-of-the-art restoration accuracy on real noise benchmarks.

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Acknowledgements. This work supported in part by the HITP grants [No.2021-0-01343, Ar- tificial Intelligence Graduate School Program (Seoul National University), No.2022-0-00156, No. 2021-0-02063 and No. no.2002--0000156], and the NRF grant [No. 2021M3A9E4000772) funded by the Korea government (MSIT), and AIRS Company in Hyundai Motor Company Kia Corporation through HMC/KIA-SNU AT Consortium Fund.
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2,5E-02
2.0E-02
P:
91
1.0E-02
5,0E-03
0.0E+00
1000
2000
3000
ISO
Figure 7: Noise distribution of images from Galaxy S6. 81 and 82 the noise levels of metadata, while å1 and B2 are the estimated noise parameters for Paasson-Gaussian distribution using NERDS. While (81, (82) have specific values at each ISO level, the radius of the circles for Bj and ßn represents the standard deviation of the estimated values for each image on SIDD training dataset.
Table Ablation study of P-G noise parameters train denoisers. Each method provides different values noise parameters for the same denoiser architecture, augmentation techniques, and training schemes. The reported scores indicate restoration performances on RGB images from the SIDD validation. NERDS-raw achieves the best PSNR score.
Metadata
Random
NERDS (Ours)
PSNR (dBWSSIM 37 37.56/09937 37.92/0.941
38.51/0.950
A
APPENDIX
A.1 DISCUSSIONS
A.1.1 NOISE DISTRIBUTION RAW IMAGES
Our NERDS assumes that the raw images have the noise that follows Poisson-Gaussia distribution. However, the image sensor of smartphone a digital camera is a black box. We do not know what kind of post-processing has been applied to the raw images, or whether the noise level (parameters) in the metadata represent the proper parameters of Poisson-Gaussian distribution. For instance, one of the noise levels in the metadata of Galaxy S6 (32 in Figure 7) equals zero for all images, which theoretically impossible for both shot and read noise parameters of the Poisson-Gauss Gaussian model. Nonetheless, Figure 7 presents a similar tendency between the noise levels in the metadata (31, 32) and the estimated noise parameters (3), 32). Thus, we approximate the noise distribution in raw images as Poisson-Gaussian noise and show that NERDS+D can achieve satisfactory denoising results using the estimated Poisson-Gaussie noise.
To show the effectiveness of accurate P-G noise parameter estimation, Table 4 presents an ablation study of the values of P-G noise parameters. NERDS-raw, Metadata, and Random represent the restoration performance of an RGB image denoiser trained with the noise parameters from each method. NERDS-raw indicates the model in Table 3(6), Metadata uses the noise levels from the metadata, and Random uniformly samples noise parameters between the maximum and minimum values in NERDS-raw. Metadata and Random the same denoiser architecture (D), augmentation techniques, and training schemes with NERDS-raw except the values of noise parameters for a fair comparison.
A.1.2 CLEAN IMAGE VIA DOWNSCALING
The optimal clean image via downscaling is a noise-free low-resolution image that has the same statistics as the true signal (clean high-resolution image). Empirically, we regard the raw images downscaled after burring (low-pas filtering) as raw pseudo-clean images. However, blurring images breaks the optimal setting when image structures are too small (e.g.. 1-pixel dots) or the noise is too severe compared to the size of the blur. Figure shows the evidence and limitations the utility of pseudo-clean images used in NERDS. First, the images downscaled without pre-blurring have
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15.51
15.51
Standaed dedasion (40))
Staddad devialaa (sS))
(3.3000)
0:34
0.03
0272
6037
24 30 18
downccaling scaling (a) wioBlur (BSD68)
dowiscaing saaing
(b) w/Blur (BSD68)
0011
32:0
0070
12.40
0.20
0009
0.30
6138
dowsscaling scaling Tockor wioBlur (SIDD)
downiceingg scoling Diker
dowascaling Scaling fotoo:
(d) w/Blur (SIDD)
(e) W/LageeBur (SSID)
Figure 8: Standard deviation (std) of downscaled images. Blue lines denote the std of clcan images while the others denote the std of noisy images. Downscaling without blu-riurning maintains the std values through scaling on both BSD68 ((a)) and SIDD ((c)). In contrast, blurring before downscaling reduces the std values of noisy images drastically than clean images ((b) (d)). (e) Large blur kernels (2x larger than kernels used in NERDS) reduce the std values more steeply.
Table 5: DxL comparison for ablation study of NERDS raw with the different sizes of blur kernels, NERDS- raw w/Blur, which the original NERDS raw, achieves the best performance.
NERDS-raw
w/eBlur
w/Blur (Ours)
w/LargeBlur
Dic
0.1710
0.0344
0.0535
similar statistics to those of the images before downscaling. This indicates that the image scaling can be regarded as capturing an image at different distances between an object and a camera. Second, the blurring operation reduces the std values of noisy images drastically more than clean images. This is why we use downscaled images as pseudo-clean images. However, blurring also reduces the std values of clean images, making it challenging to find optimal clean images. To alleviate this difficulty, estimate the noise by gradient-descent-basee optimization and adopt augmentation of noise parameter scaling (x0.5 ~ 1.5) for accurate denoiser training.
We further analyze the effectiveness of blur kernels by measuring KI. divergence between syn- thesized noisy images and real noisy images. The lower value, the better. Table visualizes an ablation study the size of blur kernels on the SIDD dataset. NERDS-raw w/Blur achieves the best performance compared to both cases of w/oBlur and w/LargeBlur. These results indicate that the downscaled images work clean images well with the proper size of blur kernels.
A.2 EFFICIENT INFERENCE AT TEST TIME
The proposed method composes three steps (noise estimation, ISP estimation, and denoiser training) and an additional step of denoiser testing. Each step described above has the latency described in Table 6. We use GeForce RTX 2080 Ti GPU and an HD image for testing.
The noise estimation, ISP estimation, and denoiser training using noisy test images are time- consuming. To skip the processes at test time, Table 7 presents a generalization test for NERDS+D trained on different datasets. NERDS+D trained on DND has accurate restoration performance on DND but performs 2 dB lower PSNR SIDD than the model trained SIDD. In contrast, NERDS+D trained on SIDD performs accurate restoration on both datasets. This phenomenon indicates that SIDD contains noise distributions similar to DND and that well-designed noisy im- ages enable generalized denoiser training. For instance, camera manufacturers can collect training datasets of only noisy images concerning the image sensor, ISP, and expected image retouching.
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Table Latency analysis of processes NERDS and NERDS+D.
Noise Estimation
ISP Estimation
Denoiser Training Denoiser Testing
Latency
30 m
h
6
0.1
Table 7: Generalization test for NERDS+D on different training datasets without clean images. The reported scores are PSNR (dB)/SSIM on RGB images from the SIDD validation and the DND henchmark. Bold denotes the hest result.
Taining
NERDS+
SIDD
DND
SIDD
38.51/0.950
36. 26/22600222
Testing
DND
39.14/0.949
39.34/0.950
A.3 COMPARISONS TO NOISE SYNTHESIS METHODS USING REAL IMAGE PAIRS
The recently proposed methods (1; 12; 56; 30) synthesize noisy images with novel noise models. These methods use noise/clean image pairs and metadata that are not accessible test time, such as the DND dataset. Although the proposed method is a general framework for denoising training with- clean image, we present comparisons with these methods by evaluating the noise estimation results with denoising performance and KL divergence.
Table 8: DxL. comparison with SotA noise synthesis methods. NERDS-raw w/clcan image outperforms all compared methods.
Calibrated (56)
Noise (1)
Camera-Aware (12)
NERDS-raw (Ours)
data Dxc.
1,5147
0.0481
0.0144
0.0079
0.0344
Table 9: Raw image denoising comparison with SotA noise synthesis methods on the SIDD benchmark. NERDS-raw achieves the best PSNR score. We use DnCNN denoiser for NERDS-raw.
Gaussian
Flow Camern- (12) NERDS-raw (Ours)
PSNR (dRySSIM
43.63/0.968
48.52/0992
48.71/0.993
48.93/0.985
Table 10: RGB image denoising comparison with SotA noise synthesis methods on the SIDD benchmark. NERDS achieves the best PSNR/SSIM scores. All methods use DrCNN as the denoiser.
Gaussian
Noise Flow
C2N (26)
RGB Noise Flow (30)
NERDS (Ours)
PSNR (dBySSIM
32.72/0.873
33.81/0.894
33.76/0.901
34.74/0.912
36.4210.923
A.3.1 RESULTS FOR NOISE ESTIMATION AND SYNTHESIS
Table 8 presents the comparisons of KL divergence (DKL) between synthesized noisy images and real noisy images. The lower values, the better. The proposed method outperforms all compared methods when using clean images. We could not reproduce the results of RGB Noise Flow (30) since the source code was not available. The score reported the paper is 0.044 for RGB Noise Flow, where Noise Flow scores 0.198. The scores of KL divergence dependent on hyperparameters.
A.3.2 RESULTS FOR RAW/RGB IMAGE DENOISING
Table 9 and 10 present denoising performances for raw/RGB images the SIDD benchmark, where NERDS raw and all methods for RGB images use DnCNN as a denoiser. Although NERDS -raw and NERDS do not clean images for noise estimation, noise synthesis, and denoiser training, NERDS-raw and NERDS achieve the best PSNR scores in each table. When converting the denoised raw images using NERDS RGB images, the PSNR is 35.74 dB which lower than RGB image denoising (36.42 dB).
A.4 ADDITIONAL ABLATION STUDY
Ablation Study for Noise Estimation (NERDS-raw). For noise estimation (NERDS-raw)) we design ablation studies on blurring strengths and downscaling factors BSD68 in Table 11 and 12.
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Table 11: Ablation study for noise estimation blurring strengths.
Noise level
woBBlur
w/Blur (Ours)
w/LargeBlur
(91, 92)
(g1, B2)
(81, B2)
(51, (2)
(0.100, 0.0200)
(0.080, 0.144)
(0.100, 0.0229)
(0.119. 0.0220)
Table 12: Ablation study for noise estimation on downscaling factors (DF).
Noise level (31, (aa
DF
DF 1.5 (Ours)
D.2.5
(b.. (2)
($1, (2)
(8i, 92)
(0.100, 0.0200)
(0.065, 0.0085)
(0.100. 0.0229)
(0.100, 0.0279)
Table 13: Ablation study for denoiser training on blurring strengths.
w/oBlur
w/Blur (Ours)
w/LargeBlur
PSNR (dB)SSIM
26.99/0.642
38.02/0.946
38.01/0.944
Table 14: Ablation study for denoiser training on blurring strengths.
DF I
DF 1.5--2.5 (Ours)
DF 2.5
PSNR (dBySSIM
37.66/0.942
38.02/0.946
37.89/0.943
The setting of w/LargeBlur uses two times larger blur kernels than NERDS-raw raww The settings of w/Blur and DF 1.5w2.5,, which indicates NERDS-raw in Table 1, present better performance than compared settings. The settings of w/oBlur or DF perform worse than those of w/LargeBlur or DF 2.5-4.5. These ablation studies show the effectiveness of image downscaling after blurring for NERDS-raw.
Ablation Study for Denoiser Training (NERDS+D). Table 13 and 14 present ablation studies on blurring strengths and downscaling factors for denoiser training on the SIDD validation. This experiment uses SIDD validation to visualize the effectiveness of downscaling and blurring to noisy images. The setting of w/LargeBlur uses two times larger blur kernels than NERDS+D. Results show that our settings for NERDS+D perform the best accuracy at the diverse DFs and blurring strengths. The setting of w/oBlur performs poor PSNR/SSIM given that the downscaled images still constrain severe noise as demonstrated in Figure 8(c). Instead, the comparable results between w/Blur and w/LarrgBllrr indicate that the denoiser training robust to blurring strengths. The settings for diverse DFs perform similar results given that blurring transforms noise from the P-G distribution. The transformed noise can be regarded as high-frequency textures that allow denoisers training for P-G noise.
A.5 ADDITIONAL QUALITATIVE RESULTS
Synthesized Noisy-Clean Image Pairs. Figure visualizes noise synthesis results using NERDS. NERDS-clean contains low-level noise while XERDS-noisy presents severe noise similar to the real noisy images. We have empirically found that denoisers do not learn to remove noise in NERDS- clean. This phenomenon is because the blurring and downscaling process in NERDS distorts the noise of raw images while denoisers learn to remove Poisson-Gaussian noise in the raw images. Moreover, given that NERDS does not require any clean data, NERDS successfully synthesizes noisy-clean image pairs on DND and MIT-Adobe FiveK datasets which are not available to access clean images.
Denoised Images. We present more images denoised by NERDS+D on SIDD (Figure 10), DND (Figure 11), and MIT-Adove FiveK (Figure 12). While AP-BSN (33), FBI-D (10), SCUNet (54), C2N (26)+DIDN (50), and CycleISP (53) often fail to restore image patterns in GT, NERDS+D successfully recover the original structures. AP-BSN uses pixel shuffle for self-supervised leamn ing to decorrelate spatially but also loses spatial information for denoising. FBI-D learns raw im- age denoising for Dbisson-Gaussian noise, but denoising raw images is an indirect approach for
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the human visual system (or RGB space). SCUNet over-smooth over-sharpen images. This restoration style helps to generate readable characters, but can distort the paaterns in GT (See Fig- ure 10). C2N+DIDN is the denoiser trained by generating noisy images from clean images using both noisy and clean images on SIDD datasets. For DND henchmarks, C2N+DIDN sometimes gen- erates artifacts noise that significantly drops the restoration accuracy (See Figure 11). In contrast, NERDS+D uses only noisy images on DND benchmarks to generate noisy-clean image pairs for accurate denoiser training. CycleISP is the denoiser trained by paired noisy-clean images on SIDD datasets and paired images synthesized by the metadata of DND benchmarks. CycleISP performs the highest PSNR and SSIM on both SIDD and DND benchmarks compared to the methods in Ta- ble 2. However, CycleISP fails to remove the noise MIT- FiveK (See Figure 12). This failure comes from the noise distribution of RGB images mismatched between MITT-dobb FiveK and SIDD (or DND) datasets.
Noisy Image
Clean Image
NERDS
NERDS-ckan
LET
LET
LET
LET
SCOR
SCOR
SCOR
SCOR
Not Available
Not Available
Figure 9: Examples noisy synthesis results using NERDS. We upscale HERDS-noisy NERDS clean, and the green boxes with the scaling factor of 2 for visualization.
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Input
GT
AP-BSN [36]
FBI-D [8]
SCUNet [43]
NERDS (Ours)
0o/1.000
29.50/0.8046
PSNR/SSIM
oz/1.000
30.41
32.77/0.9889
15.62/0.8136
33.77/0.9914
exture mapping exture e mapping exture mapping exture mapping
exture mapping
exture e mapping
constructing mo onstruccing mo conttuccting mo onstructing mo contrructing mo cnstruccing
hidden surtace hidden surface hidden surface hidden surface hidden surface hidden surface
PSNR/SSIM
as/1.000
36.95/-
35.50:0.9534
26.82/0.9643
39.56/0.9797
PSNR/SSIM
co/1.000
26.90-
29.92/0.8684
26.98/0.6723
31.22/0.9111
PSNR/SSIM
0c/1.000
36.37/-
36.73/0.9645
23.28/0.8905
38.56.9.9775
5
5
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5
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16.55/0.9250
PSNR/SSIM
os/1.000
26.49/-
28.29/0.8491
21.96/0.7389
30.76/0.9178
Figure 10: Qualitative results and PSNR (dB)/SSI on SIDD validation.
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AP-BSN [36]
C2N [19] [39)
NERDS+D (Ours)
32.94/0.8889
29.20/0.7935
34.21/0.9197
36.41/0.9539
37.42X0.9616
IN
35.87/0.9789
35.95/0.9811
36.44/0.9833
Figure 11: Qualitative results PSNR (dR)SSIM on DND.
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CycleISP(42)
FBI-D [8]
SCUNet (43]
NERDS+D (Ours)
2922
Figure Qualitative results on MIT-Adobe FiveK. CyclelSP often fails to remove noise, while FBI-D and SCUNet over-smooth or over-sharpen images. Note NERDS+ uses only 5 test noisy images estimated noise parameters from them for training. (Zooming-in for the best view.) We use T of NERDS to convert raw images denoised by FBI-D to RGB images.
20


Reviews:
[{"review_id": "rJG6j0OnGcT", "reviewer": "Reviewer_nCCv", "summary": "", "questions": "", "limitations": "", "rating": 3, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "", "summary_of_the_paper": "This paper proposes a method for real image denoising. Specifically, it firstly estimates Gaussian and Poisson noise parameters. Then, it learns a style encoder to map a raw image into rgb one. In this way, it can synthesize noisy-clean image pairs to train the denoising network. The provided experiments show the proposed method performs better than existing methods.", "summary_of_the_review": "Even though the provided experimental results are better than other methods, there exist plenty of concerns regarding noise level estimation as well as isp estimation. The authors should address them completely.", "strength_and_weaknesses": "Strength:\nThe provided experimental results show it outperforms better than other methods.\n\nWeaknesses:\n1. Even though Table 1 shows the proposed method can estimate noise parameters accurately and Figure 7 shows downscaling with blurring can make the variance difference between clean and noisy image closer, there lacks theoretical proof why the proposed method can accurately estimate noise parameters and analyzing the possible deviations in the proposed method. According to Figure 7, the variance of noisy images is still higher than clean ones. In this way, the estimated noise parameters based on Eq. 3 should be smaller.\n2. In Figure 7, why will downsampling the clean image without blurring not change the variance of the image obviously? What is the downsampling method used? Why will downsampling 2 times have a smaller variance in (a) and (c)?\n3. It is not clear whether the noise parameters are estimated for every image or for the whole dataset. According to my understanding, the proposed method estimates fixed Gaussian and Poisson parameters for the whole dataset. This is not correct. Since these parameters depend on the sensor gain or ISO. In this way, the experiment in Table 1 seems unfair. Since other methods estimate noise level image by image while the proposed one estimates it according to the whole dataset.\n4. Also, using reparameterization to estimate noise parameters may be too complicated. Why not directly search all possible Gaussian and Poisson parameters and find the settings to minimize Eq. 3?\n5. It seems the blur kernel and downsampling rate are important for the noise level estimation. Can the authors demonstrate how the proposed method selects the settings? More experiments are needed.\n6. For isp estimation, the proposed method seems to use spatially-invariant linear operators. It may be not that accurate. In the camera isp, the processing is spatially variant e.g. lens shading, local tone mapping, contrast enhancement, etc. In addition, the number of parameters in camera isp is much larger than 3 which is the number of style parameters in the proposed method. I want to know the accuracy of the generated rgb images compared with the ground truth.", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"correctness": "2: Several of the paper’s claims are incorrect or not well-supported.", "technical_novelty_and_significance": "2: The contributions are only marginally significant or novel.", "empirical_novelty_and_significance": "2: The contributions are only marginally significant or novel."}, "extra_text": {}}, {"review_id": "WceNzqVbkAF", "reviewer": "Reviewer_2oSJ", "summary": "", "questions": "", "limitations": "", "rating": 8, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "", "summary_of_the_paper": "This paper presents a novel method for training image denoising techniques, called NERDS.  The paper makes the observation that a noisy image can be downsampled to produce a pseudo-clean image, as the downsampling operation removes noise.  Based on the noise statistics between the psudeo-clean image and the original image, a noise model can be estimated in a learnable way using a reparametrization trick.  The paper also models the “black box” of an ISP disentangling style, and thereby is able to generate large amounts noisy/pseudo-clean pairs using the model and data augmentation.  These pairs are then used for training denoising methods like DnCNN.  Experimental results show the method outperforms state of the art methods.", "summary_of_the_review": "In summary, this paper is addressing a fundamental computer vision problem of removing noise from captured images.  It brings some new ideas in terms of how to model the noise from a noisy image.  Whilst the formulation might not be perfect, it provides a practical approximate way to estimate the noise which is appealing as it can be used to generate noisy/clean pairs for image denoising.  The results are appealing as the method outperforms papers recently appearing in the literature.  Overall I think the paper has merit but it’s let down a bit by overstating assumptions and remaining gaps (e.g. residual noise in the “clean” image, differences between real and “blackbox” ISPs).  Still this reviewer believes there are some interesting ideas here.", "strength_and_weaknesses": "Denoising of images is a fundamental problem in computer vision and has seen considerable attention in the last decade particularly with the advent of deep learning.  However, as argued by the paper, supervised methods require paired noisy/clean data which is difficult to obtain.  The paper addresses this problem in an appealing way by only requiring noisy images – which are easily acquired.  A clean counterpart isn’t needed, which greatly simplifies data collection for training a denoiser.  This approach is used to synthesize noisy RGB images.\n\nStrengths:\n\n1.\tThe paper’s approach that only requires noisy RGB images as input.  This is appealing as capturing paired data often involves tedious work.  The method is able to produce noisy RGB images for RGB denoisers.  This reviewer appreciates the approach of trying to generate pairs from noisy images. \n\n2.\tThe reparametrization trick to estimate the noise parameters, while simple, was interesting and something new for this reviewer.  This could be relevant for other researchers working in noise modelling and estimation. \n\n\n3.\tThe modelling of the ISP, whilst not entirely new, was useful in this paper to produce noisy RGB images for training an RGB denoiser.  The disentangling of style was appreciated by this reviewer.\n\n4.\tThe experimental results compare the method to a variety of recent methods, with the method producing convincing results.  \n\nWeaknesses:\n\n1.\tThis reviewer is concerned about the downsampling technique to create “clean” images.  Clearly the images aren’t clean – for example in Figure 1, the “clean” image in part (d) is much noisier than the clean image in part (a) of the figure.  So there is remaining noise after downsampling.  The paper doesn’t really discuss this issue, although the supplementary material provides some context.  Downsampling can’t remove the image noise because it applies a low pass filter (averaging filter) to the data.  So the noise is reduced through the averaging, but not removed.  Therefore, this reviewer would prefer if the in the main paper, instead of calling it a “clean” image, the downsampled image was called a “pseudo-clean” image as done in the supplementary.  More importantly, it would be helpful if the paper could comment on the impact of this remaining noise in the downsampled image – does it impact the results negatively?  Would downsampling by a factor of 4 help more than by a factor of 2?  This reviewer was somewhat surprised to see the results in Table 1, it does appear NERDS can produce a good fit despite the remaining noise.  Perhaps the author rebuttal could comment on this point. \n\n2.\tNoise is harder to remove in the RGB domain than the RAW domain as the statistics are simpler in the RAW domain before the noise becomes more spatially correlated due to demosacing and other operations.  Why develop a denoiser to run in the RGB domain? Conceivably the method could be used to denoise in the RAW domain.  Relatedly, it could be interesting to compare the approach to the Brooks et al. paper from CVPR 2019.\n\n\n3.\tThe reviewer is also worried about the domain gap between a “black box” ISP and the ISP that is used in the paper.  Clearly there may be a domain gap as the ISP used in the paper is only an approximation to a “black box” ISP.  Does this domain gap cause any limitations in the method?  Perhaps the proposed method does not generalize well to some phone models.\n\n4.\tWhy call it NERDS?  Is this an acronym for something?\n", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"correctness": "3: Some of the paper’s claims have minor issues. A few statements are not well-supported, or require small changes to be made correct.", "technical_novelty_and_significance": "3: The contributions are significant and somewhat new. Aspects of the contributions exist in prior work.", "empirical_novelty_and_significance": "4: The contributions are significant, and do not exist in prior works."}, "extra_text": {}}, {"review_id": "MpIhbKzHC0", "reviewer": "Reviewer_aUMk", "summary": "", "questions": "", "limitations": "", "rating": 8, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "", "summary_of_the_paper": "In this paper a learning-based camera denoising framework is introduced. Noisy raw/RGB image pairs are used for training, while the method takes a noisy RGB image only at test time for denoising. The authors designed a framework that models the raw sensor noise and ISP transformed noise separately. \n\nInstead of relying on paired noisy/clean images for training, the authors exploited the use of image downsampling and optimization-based noise estimation and synthesis to create pseudo noisy/clean image pairs at lower resolution, purely from noisy images. For estimating the Poisson-Gaussian coefficients for the raw sensor noise, a re-parameterization trick is applied and an optimization problem is solved such that the pixel variances can match between synthesized low-res noisy image and the original full-res noisy input. While P-G parameters are often provided by camera manufacturers in metadata, they may not be accurate and the authors proved that the proposed approach leads to more accurate estimation than prior arts. \n\nFor the RAW2RGB conversion, this ISP process is posed as a style transfer problem, where the style is learned from the raw-RGB noisy image pair (at original resolution) and is used to convert the raw image to RGB.  \n\nOnce the P-G model (N) and RAW2RGB conversion network T are determined, a given denoiser network can be trained on the synthesized low-res noisy/clean image pairs with a number of data augmentation tricks. The overall objective function can be seen in Eq (8).\n\nThorough qualitative, quantitive, as well as ablation studies have been conducted on various datasets, demonstrating the effectiveness of the framework, and justifying the design decisions.  \n\n", "summary_of_the_review": "While minor clarity issues remain, NERDS is a novel and effective framework for camera denoising. The main drawback (and overselling) though, seems to be the claim on requiring single noisy images only -- in practice, both the raw and RGB noisy images are required to get the best performance out of the denoiser. Please consider making the problem setting and contribution more concrete and transparent. Please also consider referencing some key justifications in the supplementary material from the main paper, as the readers may otherwise believe some design decisions lack sufficient justification (empirical, theoretic). As such, I recommend a borderline accept (6) for the work, and lean towards score 7 if these can be addressed in the revision. ", "strength_and_weaknesses": "Strength:\n- Creative way of synthesizing raw noisy/clean image pairs based on the observation that true image texture is more robust to pixel variance through downscaling than real noises. \n- Poses the ISP estimation as a style encoding and transfer problem. This, in combination with the innovation above, greatly relax the dataset requirements on real noisy/clean image pairs, ISPs, metadata etc. which are not always available or accurate especially at test time. \n- While key proposals such as pseudo clean image generation via downsampling may seem to be empirical, the authors made an attempt to justify this with data. The ablation study also seems adequate. \n- NERDS outperforms previous works with simpler denoiser network, demonstrating the effectiveness of the noisy/clean image pair synthesis, style disentanglement-based ISP estimation, as well as the rich data augmentation. \n\nWeaknesses:\n- The work still relies on raw/RGB noisy image pairs for training, although only RGB noisy image is required for denoising at test time. I would recommend that the authors rethink the title, \"NERDS: A GENERAL FRAMEWORK TO TRAIN CAMERA DENOISERS FROM SINGLE NOISY IMAGES\", as the claim on training from single noisy images may be ambiguous and potentially misleading (it actually requires both raw and RGB noisy images for training and testing ideally, thus the \"s\" in \"images\"). \n- While the authors have made an attempt to explain why the pseudo clean image (with residual noises) works in practice due to various observations / hypothesis, it may add more insights if ablation study can be conducted to assess the relative contribution from factors such as blurring strengths, downscaling factor, augmentation for noise parameter, scale/intensity, and style etc.\n- Although it is claimed by the authors that PG parameters in metadata may not be accurate, and that NERDS-raw does a better job, the audience may still appreciate more justification on this. For example, what is the impact of inaccurate PG parameters on the overall RGB denoiser performance? \n- On reproducibility, it would be helpful if the authors could clarify if the PG optimization, ISP network T training, and denoiser D training are done subsequently or jointly.  \n- I recommend that the authors consider referencing the supplementary material in the main paper, as the audience may walk away with many questions just by reading the main paper only. ", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"correctness": "3: Some of the paper’s claims have minor issues. A few statements are not well-supported, or require small changes to be made correct.", "technical_novelty_and_significance": "3: The contributions are significant and somewhat new. Aspects of the contributions exist in prior work.", "empirical_novelty_and_significance": "3: The contributions are significant and somewhat new. Aspects of the contributions exist in prior work."}, "extra_text": {}}, {"review_id": "HOamr7gvfP3", "reviewer": "Reviewer_HZ4f", "summary": "", "questions": "", "limitations": "", "rating": 8, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "", "summary_of_the_paper": "The paper proposes and evaluates a training method for neural network-based image denoising when raw images and corresponding processed (e.g. demosaiced) rgb images are available. It leverages the known observation that SNR increases when spatially downscaling an image, and thus realistic low and high-noise images can be generated at lower resolution. The method consists of several carefully chosen ingredients, e.g. (1) a scheme for estimating parameters of a Poisson-Gaussian noise model on raw images, (2) a scheme for estimating parameters of a signal processing pipeline converting raw to rgb, (3) augmentations using the estimated parameters, (4) supervised training on noisy/clean low-resolution rgb image pairs simulated from raw images. The method is evaluated for two different neural networks and compared to sota methods that have not been trained or real (hard to get) noisy/clean images. In this comparison the proposed method performs preferably.", "summary_of_the_review": "Overall a paper with merits due to its simple but novel and effective content. However, it suffers from language issues. This brings the paper well into the range of being acceptable, but keeps it from being a ‘good paper’ overall. ", "strength_and_weaknesses": "Strengths of the paper are in the well-designed, easy to understand ingredients of the method and the improved denoising performance wrt. competitors. It is overall well-structured and presents clear experiments. It also provides a rich overview on related work.\n\nMain weaknesses are language issues that sometimes lead to unclear train of thoughts within sentences or paragraphs (see examples below).\n", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"correctness": "3: Some of the paper’s claims have minor issues. A few statements are not well-supported, or require small changes to be made correct.", "technical_novelty_and_significance": "3: The contributions are significant and somewhat new. Aspects of the contributions exist in prior work.", "empirical_novelty_and_significance": "2: The contributions are only marginally significant or novel."}, "extra_text": {}}]

Author rebuttal:

Decision: Accept: poster