Deep class-aware image denoising

Remez, Tal; Litany, Or; Giryes, Raja; Bronstein, Alex

doi:10.1109/icip.2017.8296611

Cited by 27 publications

(48 citation statements)

References 60 publications

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“…Zhang et al [26] constructed a 20-layer feed-forward denoising convolutional neural networks with residual learning for Gaussian denoising. Remez et al trained 20-layer CNNs for each object category respectively and showed good performance for either Gaussian denoising [15] or Poisson denoising [16]. Zhang et al [27] proposed FFDNet adopting orthogonal regularization to enhance the generalization ability of Gaussian denoising.…”

Section: Image Denoisingmentioning

confidence: 99%

DN-ResNet: Efficient Deep Residual Network for Image Denoising

Ren

El-Khamy

Lee

2019

Lecture Notes in Computer Science

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A deep learning approach to blind denoising of images without complete knowledge of the noise statistics is considered. We propose DN-ResNet, which is a deep convolutional neural network (CNN) consisting of several residual blocks (ResBlocks). With cascade training, DN-ResNet is more accurate and more computationally efficient than the state of art denoising networks. An edge-aware loss function is further utilized in training DN-ResNet, so that the denoising results have better perceptive quality compared to conventional loss function. Next, we introduce the depthwise separable DN-ResNet (DS-DN-ResNet) utilizing the proposed Depthwise Seperable ResBlock (DS-ResBlock) instead of standard ResBlock, which has much less computational cost. DS-DN-ResNet is incrementally evolved by replacing the ResBlocks in DN-ResNet by DS-ResBlocks stage by stage. As a result, high accuracy and good computational efficiency are achieved concurrently. Whereas previous state of art deep learning methods focused on denoising either Gaussian or Poisson corrupted images, we consider denoising images having the more practical Poisson with additive Gaussian noise as well. The results show that DN-ResNets are more efficient, robust, and perform better denoising than current state of art deep learning methods, as well as the popular variants of the BM3D algorithm, in cases of blind and non-blind denoising of images corrupted with Poisson, Gaussian or Poisson-Gaussian noise. Our network also works well for other image enhancement task such as compressed image restoration.

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Section: Image Denoisingmentioning

confidence: 99%

DN-ResNet: Efficient Deep Residual Network for Image Denoising

Ren

El-Khamy

Lee

2019

Lecture Notes in Computer Science

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“…Machine learning-based techniques are widely discussed, studied and applied for image classification, image recognition, and object detection in many fields [3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21]. The related application cases of machine learning-based image detection and classification are introduced as follows.…”

Section: Related Workmentioning

confidence: 99%

“…For the other applications [15][16][17][18][19][20][21], Zhang et al [15] proposed a covariance descriptor that combined visual and geometric information. Moreover, this work integrated a classification framework with dictionary learning for the object recognition of 3D point clouds.…”

Section: Related Workmentioning

confidence: 99%

“…Moreover, this work integrated a classification framework with dictionary learning for the object recognition of 3D point clouds. Remez et al [16] presented a full CNN-based deep learning architecture for image de-noising that uses the splitting scheme to achieve sub-optimization. Nasr et al [21] used multi-class SVMs that classified facial images for robotic applications.…”

Section: Related Workmentioning

confidence: 99%

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Development and Experimental Evaluation of Machine-Learning Techniques for an Intelligent Hairy Scalp Detection System

2018

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Featured Application: Deep learning, decision tree, linear discriminant analysis (LDA), support vector machines (SVMs), k-nearest neighbors algorithm (K-NN), and ensemble learning are evaluated for detecting hairy scalp problems. To the best of our knowledge, we are the first case study to apply modern machine learning to the diagnosis and analysis of hairy scalp issues.Abstract: Deep learning has become the most popular research subject in the fields of artificial intelligence (AI) and machine learning. In October 2013, MIT Technology Review commented that deep learning was a breakthrough technology. Deep learning has made progress in voice and image recognition, image classification, and natural language processing. Prior to deep learning, decision tree, linear discriminant analysis (LDA), support vector machines (SVM), k-nearest neighbors algorithm (K-NN), and ensemble learning were popular in solving classification problems. In this paper, we applied the previously mentioned and deep learning techniques to hairy scalp images. Hairy scalp problems are usually diagnosed by non-professionals in hair salons, and people with such problems may be advised by these non-professionals. Additionally, several common scalp problems are similar; therefore, non-experts may provide incorrect diagnoses. Hence, scalp problems have worsened. In this work, we implemented and compared the deep-learning method, the ImageNet-VGG-f model Bag of Words (BOW), with machine-learning classifiers, and histogram of oriented gradients (HOG)/pyramid histogram of oriented gradients (PHOG) with machine-learning classifiers. The tools from the classification learner apps were used for hairy scalp image classification. The results indicated that deep learning can achieve an accuracy of 89.77% when the learning rate is 1 × 10 −4 , and this accuracy is far higher than those achieved by BOW with SVM (80.50%) and PHOG with SVM (53.0%).

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“…Batch normalization [20] improves the internal covariate shift by incorporating a normalization step and a scale and shift step before the nonlinearity in each layer. The merits are fast training, better performance, and low sensitivity to initialization.…”

Section: B Batch Normalizationmentioning

confidence: 99%

Sparse Residual Learning of Deep Convolution Network for De-Noising Patch Based Block Match Three Dimension Algorithm

Kamalakshi¹

2018

IJRASET

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This paper introduces a unique approach to de-noise an image based on concepts of Deep Convolution Neural Networks (DCNN) with sparse residual learning sparse reconstruction and batch normalization. The basic concept is modification of existing block match three dimension algorithm in which similar local patches in the input image are integrated into a 3D block. Here first patches are retrieved the features are extracted. The de-noised image is employed as a basic estimate for the block matching, and then de-noising function for the block is learned by a DCNN structure. Most of the residual network has many residual units (i.e., identity shortcuts), our method employs a single scarified residual unit to classify the residual image. Experimental results demonstrate the effectiveness of the sparse residual learning, sparse reconstruction and batch normalization in the tasks of image de-noising. Our experiment results proves that our model provide better efficiency in terms of PSNR. Keywords: Noise, patch, BM3D, sparse residual, sparse reconstruction batch normalization I. INTRODUCTIONRecently, image de-noising methods has gained popularity by a method called patch based method or non local means. This approach is measured an incredible in most of current state-of-the-art methods. The concept employed is to find related patterns that occur randomly all across the image and the image patches that have related patterns can be located far from each other. The patch based approach is a determining work that exploit this NSS prior [1]. The employment of patch based approach has boosted the performance of image de-noising significantly. The best example is the Block Matching and 3D Filtering (BM3D) method [2] which is a very good in performance and highly engineered approach that made the state-of-the-art record in image de-noising stay ahead for almost a decade. In past decade ,Machine learning is gaining popularity and progressively escalating its prominence. Among these deep learning concepts are overtaking shallow learning methods. It is a sort of overhyped. And very potential results have been noticed for image processing applications such as image restoration class. The significant improvement in the performance can be achieved by deep networks is due to their advanced modeling capabilities, deep structure and the adaption of non-linearities that in fact can be combined with qualified learning on large training datasets. Among all the deep learning methods, the convolutional neural networks have shown great performance for image processing tasks because of the reason of its quite easy access to large-scale dataset and the advances in deep learning methods. The proposed work is a modification of BM3D[2] [3][4] where CNN with sparse residual Learning ,sparse reconstruction and batch normalization It also adopts the residual learning formulation .

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Deep class-aware image denoising

Cited by 27 publications

References 60 publications

DN-ResNet: Efficient Deep Residual Network for Image Denoising

DN-ResNet: Efficient Deep Residual Network for Image Denoising

Development and Experimental Evaluation of Machine-Learning Techniques for an Intelligent Hairy Scalp Detection System

Sparse Residual Learning of Deep Convolution Network for De-Noising Patch Based Block Match Three Dimension Algorithm

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