scholarly journals Adaptation Strategies for Applying AWGN-Based Denoiser to Realistic Noise

2019 ◽  
Vol 33 ◽  
pp. 10085-10086
Author(s):  
Yuqian Zhou ◽  
Jianbo Jiao ◽  
Haibin Huang ◽  
Jue Wang ◽  
Thomas Huang
Keyword(s):  
Gaussian Noise ◽  
Impulse Noise ◽  
Additive White Gaussian Noise ◽  
Super Resolution ◽  
Adaptation Strategies ◽  
Noise Model ◽  
Discriminative Learning ◽  
Preliminary Results ◽  
Multi Scale ◽  
Multiple Strategies

Discriminative learning based denoising model trained with Additive White Gaussian Noise (AWGN) performs well on synthesized noise. However, realistic noise can be spatialvariant, signal-dependent and a mixture of complicated noises. In this paper, we explore multiple strategies for applying an AWGN-based denoiser to realistic noise. Specifically, we trained a deep network integrating noise estimating and denoiser with mixed Gaussian (AWGN) and Random Value Impulse Noise (RVIN). To adapt the model to realistic noises, we investigated multi-channel, multi-scale and super-resolution approaches. Our preliminary results demonstrated the effectiveness of the newly-proposed noise model and adaptation strategies.

scholarly journals When AWGN-Based Denoiser Meets Real Noises

2020 ◽  
Vol 34 (07) ◽  
pp. 13074-13081
Author(s):  
Yuqian Zhou ◽  
Jianbo Jiao ◽  
Haibin Huang ◽  
Yang Wang ◽  
Jue Wang ◽  
...  
Keyword(s):  
Gaussian Noise ◽  
Impulse Noise ◽  
State Of The Art ◽  
Additive White Gaussian Noise ◽  
White Gaussian Noise ◽  
Discriminative Learning ◽  
Real Image ◽  
Deep Model ◽  
Novel Approach ◽  
Noise Data

Discriminative learning based image denoisers have achieved promising performance on synthetic noises such as Additive White Gaussian Noise (AWGN). The synthetic noises adopted in most previous work are pixel-independent, but real noises are mostly spatially/channel-correlated and spatially/channel-variant. This domain gap yields unsatisfied performance on images with real noises if the model is only trained with AWGN. In this paper, we propose a novel approach to boost the performance of a real image denoiser which is trained only with synthetic pixel-independent noise data dominated by AWGN. First, we train a deep model that consists of a noise estimator and a denoiser with mixed AWGN and Random Value Impulse Noise (RVIN). We then investigate Pixel-shuffle Down-sampling (PD) strategy to adapt the trained model to real noises. Extensive experiments demonstrate the effectiveness and generalization of the proposed approach. Notably, our method achieves state-of-the-art performance on real sRGB images in the DND benchmark among models trained with synthetic noises. Codes are available at https://github.com/yzhouas/PD-Denoising-pytorch.

Medical image denoising using simple form of MMSE estimation in Poisson–Gaussian noise model

2016 ◽  
Vol 09 (02) ◽  
pp. 1650020 ◽  
Author(s):  
Pichid Kittisuwan
Keyword(s):  
Simple Form ◽  
Gaussian Noise ◽  
Medical Image ◽  
Additive White Gaussian Noise ◽  
Minimum Mean Square Error ◽  
Estimation Method ◽  
Imaging Systems ◽  
Noise Model ◽  
Logistic Distribution ◽  
Noise Reduction Algorithm

Poisson–Gaussian noise is the basis of image formation for a great number of imaging systems used in variety of applications, including medical and astronomical imaging. In wavelet domain, the application of Bayesian estimation method with generalized Anscombe transform in Poisson–Gaussian noise reduction algorithm has shown remarkable success over the last decade. The generalized Anscombe transform is exerted to convert the Poisson–Gaussian noise into an additive white Gaussian noise (AWGN). So, the resulting data can be denoised with any algorithm designed for the removal of AWGN. Here, we present simple form of minimum mean square error (MMSE) estimator for logistic distribution in Poisson–Gaussian noise. The experimental results show that the proposed method yields good denoising results.

scholarly journals Hyperspectral Unmixing with Bandwise Generalized Bilinear Model

2018 ◽  
Vol 10 (10) ◽  
pp. 1600 ◽  
Author(s):  
Chang Li ◽  
Yu Liu ◽  
Juan Cheng ◽  
Rencheng Song ◽  
Hu Peng ◽  
...  
Keyword(s):  
Gaussian Noise ◽  
Impulse Noise ◽  
Hyperspectral Image ◽  
State Of The Art ◽  
Additive White Gaussian Noise ◽  
Bilinear Model ◽  
Hyperspectral Unmixing ◽  
Noise Impulse ◽  
Mixed Noise ◽  
Alternative Direction Method

Generalized bilinear model (GBM) has received extensive attention in the field of hyperspectral nonlinear unmixing. Traditional GBM unmixing methods are usually assumed to be degraded only by additive white Gaussian noise (AWGN), and the intensity of AWGN in each band of hyperspectral image (HSI) is assumed to be the same. However, the real HSIs are usually degraded by mixture of various kinds of noise, which include Gaussian noise, impulse noise, dead pixels or lines, stripes, and so on. Besides, the intensity of AWGN is usually different for each band of HSI. To address the above mentioned issues, we propose a novel nonlinear unmixing method based on the bandwise generalized bilinear model (NU-BGBM), which can be adapted to the presence of complex mixed noise in real HSI. Besides, the alternative direction method of multipliers (ADMM) is adopted to solve the proposed NU-BGBM. Finally, extensive experiments are conducted to demonstrate the effectiveness of the proposed NU-BGBM compared with some other state-of-the-art unmixing methods.

scholarly journals Forgery Detection in Digital Images by Multi-Scale Noise Estimation

2021 ◽  
Vol 7 (7) ◽  
pp. 119
Author(s):  
Marina Gardella ◽  
Pablo Musé ◽  
Jean-Michel Morel ◽  
Miguel Colom
Keyword(s):  
Noise Model ◽  
Correlated Noise ◽  
Forgery Detection ◽  
Color Channel ◽  
Image Block ◽  
Multi Scale ◽  
Compressed Images ◽  
Inconsistency Analysis ◽  
Highly Correlated ◽  
The Moment

A complex processing chain is applied from the moment a raw image is acquired until the final image is obtained. This process transforms the originally Poisson-distributed noise into a complex noise model. Noise inconsistency analysis is a rich source for forgery detection, as forged regions have likely undergone a different processing pipeline or out-camera processing. We propose a multi-scale approach, which is shown to be suitable for analyzing the highly correlated noise present in JPEG-compressed images. We estimate a noise curve for each image block, in each color channel and at each scale. We then compare each noise curve to its corresponding noise curve obtained from the whole image by counting the percentage of bins of the local noise curve that are below the global one. This procedure yields crucial detection cues since many forgeries create a local noise deficit. Our method is shown to be competitive with the state of the art. It outperforms all other methods when evaluated using the MCC score, or on forged regions large enough and for colorization attacks, regardless of the evaluation metric.

scholarly journals Threshold Computation for Spatially Coupled Turbo-Like Codes on the AWGN Channel

Entropy ◽  
2021 ◽  
Vol 23 (2) ◽  
pp. 240
Author(s):  
Muhammad Umar Farooq ◽  
Alexandre Graell i Amat ◽  
Michael Lentmaier
Keyword(s):  
Gaussian Noise ◽  
Belief Propagation ◽  
Additive White Gaussian Noise ◽  
Maximum A Posteriori ◽  
Prediction Methods ◽  
Threshold Analysis ◽  
A Posteriori ◽  
Awgn Channel ◽  
Efficient Prediction ◽  
Binary Erasure Channel

In this paper, we perform a belief propagation (BP) decoding threshold analysis of spatially coupled (SC) turbo-like codes (TCs) (SC-TCs) on the additive white Gaussian noise (AWGN) channel. We review Monte-Carlo density evolution (MC-DE) and efficient prediction methods, which determine the BP thresholds of SC-TCs over the AWGN channel. We demonstrate that instead of performing time-consuming MC-DE computations, the BP threshold of SC-TCs over the AWGN channel can be predicted very efficiently from their binary erasure channel (BEC) thresholds. From threshold results, we conjecture that the similarity of MC-DE and predicted thresholds is related to the threshold saturation capability as well as capacity-approaching maximum a posteriori (MAP) performance of an SC-TC ensemble.

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