2014
DOI: 10.14257/ijsip.2014.7.3.14
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Deep Learning for Image Denoising

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Cited by 25 publications
(12 citation statements)
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“…[5] proposed a natural image enhancement and denoising method using convolutional networks. [6] proposed a multi-layer stacking self-coding method for image processing.…”
Section: Related Workmentioning
confidence: 99%
“…[5] proposed a natural image enhancement and denoising method using convolutional networks. [6] proposed a multi-layer stacking self-coding method for image processing.…”
Section: Related Workmentioning
confidence: 99%
“…13,24,25 Similar to other feed-forward artificial neural networks (ANNs), DL employs more than one hidden layer (y) that connects the input (x) and output layer (z) via a weight (W) matrix as shown in eq 2. Here we used sigmoid function as the activation function:…”
Section: Data Preprocessingmentioning
confidence: 99%
“…Several state-of-the-art denoising methods with default parameters are used for comparison with the proposed DPLG algorithm, including the internal denosing methods B-M3D [10], K-SVD [11] and NCSR [12], the external denoising methods SSDA [13] and SDAE [14], SCLW [15], and NSCDL [16]. As for the parameter setting of our DPLG algorithm, the k1-nearest neighbor parameter, the maximum depth of leaf nodes and the number of iterations of the DPLG are empirically set to 6, 7 and 18, respectively, from a series of tentative test.…”
Section: B Experiments On Benchmark Imagesmentioning
confidence: 99%
“…Over the past decades, many denoising methods have been proposed for reconstructing the original image from the observed noisy image by exploiting the inherently spatial correlations [5]- [12]. The image denoising methods are generally divided into three categories including (i) internal denoising methods (e.g., BM3D [5], K-SVD [11], NCSR [12]): using only the noisy image patches from a single noisy image; (ii) external denoising methods (e.g., SSDA [13], SDAE [14]): training the mapping from noisy images to clean images using only external clean image patches; and (iii) internal-external denoising methods (e.g. SCLW [15], NSCDL [16]): jointly using the external statistics information from a clean training image set and the internal statistics information from the observed noisy image.…”
Section: Introductionmentioning
confidence: 99%
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