A convolutional neural networks denoising approach for salt and pepper noise

Fu, Bo; Zhao, Xianfeng; Li, Yang; Wang, Xianghai; Ren, Yonggong

doi:10.1007/s11042-018-6521-4

Cited by 39 publications

(27 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This confirms the potential of such operators for filtering problems, and the interest of being able to train them end-to-end for a given filtering task. To confirm the interest of DMNN for denoising, we conduct the same denoising experiment on natural images from the Berkeley Segmentation Dataset BSDS500 [55], for an experiment similar to the one conducted in [54]. The noise is applied randomly on each channel of the considered RGB images.…”

Section: Image Denoisingmentioning

confidence: 99%

Deep morphological networks

Franchi

Fehri²,

Yao

2020

Pattern Recognition

View full text Add to dashboard Cite

Mathematical morphology provides powerful nonlinear operators for a variety of image processing tasks such as filtering, segmentation, and edge detection. In this paper, we propose a way to use these nonlinear operators in an end-toend deep learning framework and illustrate them on different applications. We demonstrate on various examples that new layers making use of the morphological non-linearities are complementary to convolution layers. These new layers can be used to integrate the non-linear operations and pooling into a joint operation. We finally enhance results obtained in boundary detection using this new family of layers with just 0.01% of the parameters of competing state-of-the-art methods.

show abstract

Section: Image Denoisingmentioning

confidence: 99%

Deep morphological networks

Franchi

Fehri²,

Yao

2020

Pattern Recognition

View full text Add to dashboard Cite

show abstract

“…The recently proposed filters significantly outperform classical, well established algorithms in terms of filtering efficiency and computational speed [44][45][46]. However, despite the fact that image denoising using deep learning for Gaussian noise removal has been well-studied, relatively little work has been done in the area of impulsive noise detection and removal [47][48][49][50][51]. In [47], the authors proposed a method, which replaces noisy image pixels by a weighted average of samples from the neighborhood to remove salt and pepper noise and then the filter output is further processed using CNN to boost the final filtering performance.…”

Section: Introductionmentioning

confidence: 99%

“…However, despite the fact that image denoising using deep learning for Gaussian noise removal has been well-studied, relatively little work has been done in the area of impulsive noise detection and removal [47][48][49][50][51]. In [47], the authors proposed a method, which replaces noisy image pixels by a weighted average of samples from the neighborhood to remove salt and pepper noise and then the filter output is further processed using CNN to boost the final filtering performance. An alternative technique [48] divides the input image into small patches, which are processed independently by a set of convolution and deconvolution layers.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning Based Switching Filter for Impulsive Noise Removal in Color Images

Radlak

Maliński

Smołka

2020

Sensors

View full text Add to dashboard Cite

Noise reduction is one of the most important and still active research topics in low-level image processing due to its high impact on object detection and scene understanding for computer vision systems. Recently, we observed a substantially increased interest in the application of deep learning algorithms. Many computer vision systems use them, due to their impressive capability of feature extraction and classification. While these methods have also been successfully applied in image denoising, significantly improving its performance, most of the proposed approaches were designed for Gaussian noise suppression. In this paper, we present a switching filtering technique intended for impulsive noise removal using deep learning. In the proposed method, the distorted pixels are detected using a deep neural network architecture and restored with the fast adaptive mean filter. The performed experiments show that the proposed approach is superior to the state-of-the-art filters designed for impulsive noise removal in color digital images.

show abstract

“…The size of the convolution kernel is adaptive with maximum size 11 × 11, enabling the method to be able to achieve an acceptable performance. Fu et al [28] proposed using a wide‐range switching filter CNN for the removal of SAP noise in noisy images. First, this method uses a wide‐range switching filter as a pre‐processing step, where most of the noise can be removed.…”

Section: Introductionmentioning

confidence: 99%

Image enhancement using convolutional neural network to identify similar patterns

Chen

Wang

et al. 2020

IET Image Processing

View full text Add to dashboard Cite

An image may be disturbed by impulse noise during transmission or acquisition. To effectively restore the disturbed image is important for the applications of image processing. This study aims at enhancing the disturbed images by using the convolutional neural network (CNN) to identify similar patterns for the restoration of noisy pixels. In the training phase, each noisy pixel is analysed and compared with the noise-free image to find the closest neighbouring pixels. The pixels in a local window form a micro-pattern. All the captured micro-patterns, whose centre pixel is noisy, become a dataset for the training of a position CNN. The closest neighbouring pixel of a noisy image to the centre one of the noise-free image at the same position of each micro-pattern is selected to be the target. In the enhancement phase, a noisy micro-pattern, where the centre pixel is noisy, is input into the trained position CNN. The top N pixels are recognised and averaged to replace the grey level of the centre pixel. An enhanced pixel is obtained. The experimental results show that the position CNN can well recognise the similar neighbouring pixels and effectively enhance the noisy pixels in an image disturbed by salt-and-pepper noise.

show abstract

A convolutional neural networks denoising approach for salt and pepper noise

Cited by 39 publications

References 33 publications

Deep morphological networks

Deep morphological networks

Deep Learning Based Switching Filter for Impulsive Noise Removal in Color Images

Image enhancement using convolutional neural network to identify similar patterns

Contact Info

Product

Resources

About