l0Sparsity for Image Denoising with Local and Global Priors

We propose al0sparsity based approach to remove additive white Gaussian noise from a given image. To achieve this goal, we combine the local prior and global prior together to recover the noise-free values of pixels. The local prior depends on the neighborhood relationships of a search window to help maintain edges and smoothness. The global prior is generated from a hierarchicall0sparse representation to help eliminate the redundant information and preserve the global consistency. In addition, to make the correlations between pixels more meaningful, we adopt Principle Component Analysis to measure the similarities, which can be both propitious to reduce the computational complexity and improve the accuracies. Experiments on the benchmark image set show that the proposed approach can achieve superior performance to the state-of-the-art approaches both in accuracy and perception in removing the zero-mean additive white Gaussian noise.

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SAR Image Despeckling by Deep Neural Networks: from a Pre-Trained Model to an End-to-End Training Strategy

Remote Sensing ◽

10.3390/rs12162636 ◽

2020 ◽

Vol 12 (16) ◽

pp. 2636

Author(s):

Emanuele Dalsasso ◽

Xiangli Yang ◽

Loïc Denis ◽

Florence Tupin ◽

Wen Yang

Keyword(s):

Neural Networks ◽

Gaussian Noise ◽

State Of The Art ◽

Additive White Gaussian Noise ◽

Hybrid Approach ◽

White Gaussian Noise ◽

Training Data ◽

Speckle Reduction ◽

Sar Image ◽

Sar Images

Speckle reduction is a longstanding topic in synthetic aperture radar (SAR) images. Many different schemes have been proposed for the restoration of intensity SAR images. Among the different possible approaches, methods based on convolutional neural networks (CNNs) have recently shown to reach state-of-the-art performance for SAR image restoration. CNN training requires good training data: many pairs of speckle-free/speckle-corrupted images. This is an issue in SAR applications, given the inherent scarcity of speckle-free images. To handle this problem, this paper analyzes different strategies one can adopt, depending on the speckle removal task one wishes to perform and the availability of multitemporal stacks of SAR data. The first strategy applies a CNN model, trained to remove additive white Gaussian noise from natural images, to a recently proposed SAR speckle removal framework: MuLoG (MUlti-channel LOgarithm with Gaussian denoising). No training on SAR images is performed, the network is readily applied to speckle reduction tasks. The second strategy considers a novel approach to construct a reliable dataset of speckle-free SAR images necessary to train a CNN model. Finally, a hybrid approach is also analyzed: the CNN used to remove additive white Gaussian noise is trained on speckle-free SAR images. The proposed methods are compared to other state-of-the-art speckle removal filters, to evaluate the quality of denoising and to discuss the pros and cons of the different strategies. Along with the paper, we make available the weights of the trained network to allow its usage by other researchers.

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When AWGN-Based Denoiser Meets Real Noises

Proceedings of the AAAI Conference on Artificial Intelligence ◽

10.1609/aaai.v34i07.7009 ◽

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.

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