A High-Quality Denoising Dataset for Smartphone Cameras

Abdelhamed, Abdelrahman; Lin, Stephen; Brown, Michael S.

doi:10.1109/cvpr.2018.00182

Cited by 648 publications

(489 citation statements)

References 29 publications

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“…To assess the performance of Noise Flow, we train it to model the realistic noise distribution of the Smartphone Image Denoising Dataset (SIDD) [1] and also evaluate the sampling accuracy of the trained model.…”

Section: Methodsmentioning

confidence: 99%

“…Signal-dependent models may accurately describe noise components, such as photon noise. However, in real images there are still other noise sources that may not be accurately represented by such models [1,7,26]. Examples of such sources include fixed-pattern noise, defective pixels, clipped intensities, spatially correlated noise (i.e., crosstalk), amplification, and quantization noise.…”

Section: Background and Related Workmentioning

confidence: 99%

“…We use the DnCNN image denoiser [33]. We use the clean images from the SIDD-Medium [1] as training ground truth and the SIDD-Validation as our testing set. The SIDD-Validation contains both real noisy images and the corresponding ground truth.…”

Section: Application To Real Image Denoisingmentioning

confidence: 99%

“…The signal-dependent and gain layers are newly proposed. The Raw-to-sRGB pipeline is ported from[1].…”

mentioning

confidence: 99%

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Noise Flow: Noise Modeling With Conditional Normalizing Flows

Abdelhamed

Brubaker

Brown

2019

2019 IEEE/CVF International Conference on Computer Vision (ICCV)

Self Cite

167

129

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Modeling and synthesizing image noise is an important aspect in many computer vision applications. The long-standing additive white Gaussian and heteroscedastic (signal-dependent) noise models widely used in the literature provide only a coarse approximation of real sensor noise. This paper introduces Noise Flow, a powerful and accurate noise model based on recent normalizing flow architectures. Noise Flow combines well-established basic parametric noise models (e.g., signal-dependent noise) with the flexibility and expressiveness of normalizing flow networks. The result is a single, comprehensive, compact noise model containing fewer than 2500 parameters yet able to represent multiple cameras and gain factors. Noise Flow dramatically outperforms existing noise models, with 0.42 nats/pixel improvement over the camera-calibrated noise level functions, which translates to 52% improvement in the likelihood of sampled noise. Noise Flow represents the first serious attempt to go beyond simple parametric models to one that leverages the power of deep learning and datadriven noise distributions.

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Section: Methodsmentioning

confidence: 99%

Section: Background and Related Workmentioning

confidence: 99%

Section: Application To Real Image Denoisingmentioning

confidence: 99%

“…The signal-dependent and gain layers are newly proposed. The Raw-to-sRGB pipeline is ported from[1].…”

mentioning

confidence: 99%

See 2 more Smart Citations

Noise Flow: Noise Modeling With Conditional Normalizing Flows

Abdelhamed

Brubaker

Brown

2019

2019 IEEE/CVF International Conference on Computer Vision (ICCV)

Self Cite

167

129

View full text Add to dashboard Cite

show abstract

“…For training, we utilize 320 image pairs (noisy and clean) both in raw-RGB space and sRGB space from Smartphone Image Denoising Dataset (SIDD) [14]. And we set another 1280 256 × 256 crops of 40 images in SIDD as our validation data to conduct our ablation study.…”

Section: A Datasetsmentioning

confidence: 99%

Pyramid Real Image Denoising Network

Zhao

Jiang

Men

et al. 2019

2019 IEEE Visual Communications and Image Processing (VCIP)

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While deep Convolutional Neural Networks (CNNs) have shown extraordinary capability of modelling specific noise and denoising, they still perform poorly on real-world noisy images. The main reason is that the real-world noise is more sophisticated and diverse. To tackle the issue of blind denoising, in this paper, we propose a novel pyramid real image denoising network (PRIDNet), which contains three stages. First, the noise estimation stage uses channel attention mechanism to recalibrate the channel importance of input noise. Second, at the multi-scale denoising stage, pyramid pooling is utilized to extract multi-scale features. Third, the stage of feature fusion adopts a kernel selecting operation to adaptively fuse multi-scale features. Experiments on two datasets of real noisy photographs demonstrate that our approach can achieve competitive performance in comparison with state-of-the-art denoisers in terms of both quantitative measure and visual perception quality. Code is available at https://github.com/491506870/PRIDNet.

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Deep learning‐based super‐resolution with feature coordinators preservation for vision‐based measurement

Wang

Lin

et al. 2022

Structural Contr & Hlth

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Vision-based displacement measurement is promising and emerging for structural monitoring. However, the accuracy of visual measurement is commonly limited by the resolution of the camera. The super-resolution (SR) technique can reconstruct high-resolution images from the corresponding low-resolution images within the constraints of prior knowledge. Existing SR algorithms mainly focus on improving the overall quality of the image. By contrast, the accurate extraction of the coordinates of feature points is the most important for the visual measurement. Besides, the SR network is usually trained by an artificial dataset whose low-resolution images are obtained by artificially degrading the corresponding high-resolution images, instead of those directly captured by cameras. However, this degradation usually is only a simple bicubic downsampling that cannot reflect the real degradation, which will provide inaccurate constraints to the network training. Therefore, this paper proposes a novel SR framework that can significantly preserve the feature coordinators for visual measurement (SRFCP). First, a deep learning-based SR network that focuses on feature preservation is proposed, which introduces both feature weighted branch and feature preserving loss. Second, an image degradation model is built based on the blur kernel and noise extracted from the images captured in real scene. Experiments on public datasets show that the proposed SRFCP performs well both in terms of the objective evaluation index and the subjective visual effect. Then, a binocular visual measurement platform is set up to measure the distance of adjacent feature points on a chessboard. Lastly, several SR algorithms are evaluated by the improvement they bring to the measurement accuracy. Experimental results show that the distance measurement performance can be significantly improved by the images reconstructed by the SRFCP. Therefore, the proposed SRFCP can accurately reconstruct the high-resolution images while preserving the features coordinates, which is crucial for the visual measurement in structural monitoring.

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A High-Quality Denoising Dataset for Smartphone Cameras

Cited by 648 publications

References 29 publications

Noise Flow: Noise Modeling With Conditional Normalizing Flows

Noise Flow: Noise Modeling With Conditional Normalizing Flows

Pyramid Real Image Denoising Network

Deep learning‐based super‐resolution with feature coordinators preservation for vision‐based measurement

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