The Experts below are selected from a list of 102855 Experts worldwide ranked by ideXlab platform
Kaoning Hu - One of the best experts on this subject based on the ideXlab platform.
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ICTAI - Rethink Gaussian Denoising Prior for Real-World Image Denoising
2019 IEEE 31st International Conference on Tools with Artificial Intelligence (ICTAI), 2019Co-Authors: Tianyang Wang, Bo Li, Jun Huan, Kaoning HuAbstract:Real-world image Denoising is a challenging but significant problem in computer vision. Unlike Gaussian Denoising on which most existing methods focus, the real-world noise is nonadditive, and the distributions are difficult to model. This leads to unsatisfactory performance when applying a Gaussian Denoising approach on real-world Denoising problems. In this paper, we propose a simple framework for effective real-world image Denoising. Specifically, we investigate the intrinsic properties of the Gaussian Denoising prior and demonstrate this prior can aid real-world image Denoising. To leverage this prior, we fine-tune it for only one epoch on a recently proposed real-world image Denoising dataset, and the learned model can enhance both visual and quantitative results (peak-signal-noise-ratio) for real-world image Denoising tasks. Extensive experiments demonstrate the effectiveness of our approach, and indicate that the Gaussian Denoising prior can be also transferred to real-world image Denoising by exploiting appropriate training schemes.
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Rethink Gaussian Denoising Prior for Real-World Image Denoising
2019 IEEE 31st International Conference on Tools with Artificial Intelligence (ICTAI), 2019Co-Authors: Tianyang Wang, Bo Li, Jun Huan, Kaoning HuAbstract:Real-world image Denoising is a challenging but significant problem in computer vision. Unlike Gaussian Denoising on which most existing methods focus, the real-world noise is nonadditive, and the distributions are difficult to model. This leads to unsatisfactory performance when applying a Gaussian Denoising approach on real-world Denoising problems. In this paper, we propose a simple framework for effective real-world image Denoising. Specifically, we investigate the intrinsic properties of the Gaussian Denoising prior and demonstrate this prior can aid real-world image Denoising. To leverage this prior, we fine-tune it for only one epoch on a recently proposed real-world image Denoising dataset, and the learned model can enhance both visual and quantitative results (peak-signal-noise-ratio) for real-world image Denoising tasks. Extensive experiments demonstrate the effectiveness of our approach, and indicate that the Gaussian Denoising prior can be also transferred to real-world image Denoising by exploiting appropriate training schemes.
Tianyang Wang - One of the best experts on this subject based on the ideXlab platform.
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ICTAI - Rethink Gaussian Denoising Prior for Real-World Image Denoising
2019 IEEE 31st International Conference on Tools with Artificial Intelligence (ICTAI), 2019Co-Authors: Tianyang Wang, Bo Li, Jun Huan, Kaoning HuAbstract:Real-world image Denoising is a challenging but significant problem in computer vision. Unlike Gaussian Denoising on which most existing methods focus, the real-world noise is nonadditive, and the distributions are difficult to model. This leads to unsatisfactory performance when applying a Gaussian Denoising approach on real-world Denoising problems. In this paper, we propose a simple framework for effective real-world image Denoising. Specifically, we investigate the intrinsic properties of the Gaussian Denoising prior and demonstrate this prior can aid real-world image Denoising. To leverage this prior, we fine-tune it for only one epoch on a recently proposed real-world image Denoising dataset, and the learned model can enhance both visual and quantitative results (peak-signal-noise-ratio) for real-world image Denoising tasks. Extensive experiments demonstrate the effectiveness of our approach, and indicate that the Gaussian Denoising prior can be also transferred to real-world image Denoising by exploiting appropriate training schemes.
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Rethink Gaussian Denoising Prior for Real-World Image Denoising
2019 IEEE 31st International Conference on Tools with Artificial Intelligence (ICTAI), 2019Co-Authors: Tianyang Wang, Bo Li, Jun Huan, Kaoning HuAbstract:Real-world image Denoising is a challenging but significant problem in computer vision. Unlike Gaussian Denoising on which most existing methods focus, the real-world noise is nonadditive, and the distributions are difficult to model. This leads to unsatisfactory performance when applying a Gaussian Denoising approach on real-world Denoising problems. In this paper, we propose a simple framework for effective real-world image Denoising. Specifically, we investigate the intrinsic properties of the Gaussian Denoising prior and demonstrate this prior can aid real-world image Denoising. To leverage this prior, we fine-tune it for only one epoch on a recently proposed real-world image Denoising dataset, and the learned model can enhance both visual and quantitative results (peak-signal-noise-ratio) for real-world image Denoising tasks. Extensive experiments demonstrate the effectiveness of our approach, and indicate that the Gaussian Denoising prior can be also transferred to real-world image Denoising by exploiting appropriate training schemes.
Bo Li - One of the best experts on this subject based on the ideXlab platform.
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ICTAI - Rethink Gaussian Denoising Prior for Real-World Image Denoising
2019 IEEE 31st International Conference on Tools with Artificial Intelligence (ICTAI), 2019Co-Authors: Tianyang Wang, Bo Li, Jun Huan, Kaoning HuAbstract:Real-world image Denoising is a challenging but significant problem in computer vision. Unlike Gaussian Denoising on which most existing methods focus, the real-world noise is nonadditive, and the distributions are difficult to model. This leads to unsatisfactory performance when applying a Gaussian Denoising approach on real-world Denoising problems. In this paper, we propose a simple framework for effective real-world image Denoising. Specifically, we investigate the intrinsic properties of the Gaussian Denoising prior and demonstrate this prior can aid real-world image Denoising. To leverage this prior, we fine-tune it for only one epoch on a recently proposed real-world image Denoising dataset, and the learned model can enhance both visual and quantitative results (peak-signal-noise-ratio) for real-world image Denoising tasks. Extensive experiments demonstrate the effectiveness of our approach, and indicate that the Gaussian Denoising prior can be also transferred to real-world image Denoising by exploiting appropriate training schemes.
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Rethink Gaussian Denoising Prior for Real-World Image Denoising
2019 IEEE 31st International Conference on Tools with Artificial Intelligence (ICTAI), 2019Co-Authors: Tianyang Wang, Bo Li, Jun Huan, Kaoning HuAbstract:Real-world image Denoising is a challenging but significant problem in computer vision. Unlike Gaussian Denoising on which most existing methods focus, the real-world noise is nonadditive, and the distributions are difficult to model. This leads to unsatisfactory performance when applying a Gaussian Denoising approach on real-world Denoising problems. In this paper, we propose a simple framework for effective real-world image Denoising. Specifically, we investigate the intrinsic properties of the Gaussian Denoising prior and demonstrate this prior can aid real-world image Denoising. To leverage this prior, we fine-tune it for only one epoch on a recently proposed real-world image Denoising dataset, and the learned model can enhance both visual and quantitative results (peak-signal-noise-ratio) for real-world image Denoising tasks. Extensive experiments demonstrate the effectiveness of our approach, and indicate that the Gaussian Denoising prior can be also transferred to real-world image Denoising by exploiting appropriate training schemes.
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Salt & pepper image Denoising based on Adaptive Fractional integral
2016 Chinese Control and Decision Conference (CCDC), 2016Co-Authors: Bo Li, Langwen Zhang, Zhongwei HeAbstract:The traditional Denoising algorithms may easily neglect image textures. To deal with this problem, this paper proposes an image Denoising algorithm based on Adaptive Fractional integral (AFIID) for salt & pepper noise images. This algorithm uses the intensity of noise and the gradient of each pixel to calculate the gradient threshold of noise. It then finds the optimal fractional integral order of each noise point depending on their average gradient. And taking convolution with the adaptive fractional integral mask and images will preserve images' texture information while image Denoising. Experiments shows that compared with traditional image Denoising methods, AFIID algorithm has a better effect in Denoising and preserving image textures.
Jun Huan - One of the best experts on this subject based on the ideXlab platform.
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ICTAI - Rethink Gaussian Denoising Prior for Real-World Image Denoising
2019 IEEE 31st International Conference on Tools with Artificial Intelligence (ICTAI), 2019Co-Authors: Tianyang Wang, Bo Li, Jun Huan, Kaoning HuAbstract:Real-world image Denoising is a challenging but significant problem in computer vision. Unlike Gaussian Denoising on which most existing methods focus, the real-world noise is nonadditive, and the distributions are difficult to model. This leads to unsatisfactory performance when applying a Gaussian Denoising approach on real-world Denoising problems. In this paper, we propose a simple framework for effective real-world image Denoising. Specifically, we investigate the intrinsic properties of the Gaussian Denoising prior and demonstrate this prior can aid real-world image Denoising. To leverage this prior, we fine-tune it for only one epoch on a recently proposed real-world image Denoising dataset, and the learned model can enhance both visual and quantitative results (peak-signal-noise-ratio) for real-world image Denoising tasks. Extensive experiments demonstrate the effectiveness of our approach, and indicate that the Gaussian Denoising prior can be also transferred to real-world image Denoising by exploiting appropriate training schemes.
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Rethink Gaussian Denoising Prior for Real-World Image Denoising
2019 IEEE 31st International Conference on Tools with Artificial Intelligence (ICTAI), 2019Co-Authors: Tianyang Wang, Bo Li, Jun Huan, Kaoning HuAbstract:Real-world image Denoising is a challenging but significant problem in computer vision. Unlike Gaussian Denoising on which most existing methods focus, the real-world noise is nonadditive, and the distributions are difficult to model. This leads to unsatisfactory performance when applying a Gaussian Denoising approach on real-world Denoising problems. In this paper, we propose a simple framework for effective real-world image Denoising. Specifically, we investigate the intrinsic properties of the Gaussian Denoising prior and demonstrate this prior can aid real-world image Denoising. To leverage this prior, we fine-tune it for only one epoch on a recently proposed real-world image Denoising dataset, and the learned model can enhance both visual and quantitative results (peak-signal-noise-ratio) for real-world image Denoising tasks. Extensive experiments demonstrate the effectiveness of our approach, and indicate that the Gaussian Denoising prior can be also transferred to real-world image Denoising by exploiting appropriate training schemes.
Lei Zhang - One of the best experts on this subject based on the ideXlab platform.
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ffdnet toward a fast and flexible solution for cnn based image Denoising
IEEE Transactions on Image Processing, 2018Co-Authors: Kai Zhang, Lei ZhangAbstract:Due to the fast inference and good performance, discriminative learning methods have been widely studied in image Denoising. However, these methods mostly learn a specific model for each noise level, and require multiple models for Denoising images with different noise levels. They also lack flexibility to deal with spatially variant noise, limiting their applications in practical Denoising. To address these issues, we present a fast and flexible Denoising convolutional neural network, namely FFDNet, with a tunable noise level map as the input. The proposed FFDNet works on downsampled sub-images, achieving a good trade-off between inference speed and Denoising performance. In contrast to the existing discriminative denoisers, FFDNet enjoys several desirable properties, including: 1) the ability to handle a wide range of noise levels (i.e., [0, 75]) effectively with a single network; 2) the ability to remove spatially variant noise by specifying a non-uniform noise level map; and 3) faster speed than benchmark BM3D even on CPU without sacrificing Denoising performance. Extensive experiments on synthetic and real noisy images are conducted to evaluate FFDNet in comparison with state-of-the-art denoisers. The results show that FFDNet is effective and efficient, making it highly attractive for practical Denoising applications.
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beyond a gaussian denoiser residual learning of deep cnn for image Denoising
IEEE Transactions on Image Processing, 2017Co-Authors: Kai Zhang, Wangmeng Zuo, Yunjin Chen, Deyu Meng, Lei ZhangAbstract:The discriminative model learning for image Denoising has been recently attracting considerable attentions due to its favorable Denoising performance. In this paper, we take one step forward by investigating the construction of feed-forward Denoising convolutional neural networks (DnCNNs) to embrace the progress in very deep architecture, learning algorithm, and regularization method into image Denoising. Specifically, residual learning and batch normalization are utilized to speed up the training process as well as boost the Denoising performance. Different from the existing discriminative Denoising models which usually train a specific model for additive white Gaussian noise at a certain noise level, our DnCNN model is able to handle Gaussian Denoising with unknown noise level (i.e., blind Gaussian Denoising). With the residual learning strategy, DnCNN implicitly removes the latent clean image in the hidden layers. This property motivates us to train a single DnCNN model to tackle with several general image Denoising tasks, such as Gaussian Denoising, single image super-resolution, and JPEG image deblocking. Our extensive experiments demonstrate that our DnCNN model can not only exhibit high effectiveness in several general image Denoising tasks, but also be efficiently implemented by benefiting from GPU computing.
