An image topic model for image denoising

Fu, Bo; Li, Weiwei; Fu, You-Ping; Song, Chuanming

doi:10.1016/j.neucom.2014.11.094

Cited by 11 publications

(5 citation statements)

References 17 publications

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“…The existence of noise affect the subsequent analysis and processing of the image, and also reduce the accuracy of image targer recognition. In order to reduce the influence of noise on images, appropriate filtering algorithms can be adopted to denoise different types of noised images.The classical denoising algorithms mainly include mean filtering, Wiener filtering, median filtering, etc., and a lot of research work has been carried out in the field of image denoising [1][2][3][4][5][6][7][8][9][10] .…”

Section: Introductionmentioning

confidence: 99%

An adaptive mean denoising algorithm for pepper and salt noised image

Qin

Wei

Liu

et al. 2023

Fourteenth International Conference on Graphics and Image Processing (ICGIP 2022)

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To improve the quality of the pepper and salt noised image, an adaptive mean filter algorithm is proposed. Firstly, the appropriate filter window size is automatically selected according to the intensity of noise in the image. Secondly, whether each pixel is a pepper and salt noise point is detected . The noised point outputs the mean gray value of the unnoised pixels in the current window. The non-noised point outputs the original value. Thirdly, the denoising effect of the three algorithms was compared by simulation experiments, and the influence of different pepper and salt noise density on denoising effect was tested. From the objective and subjective judgement, the propose method achieves higher PSNR and better visual effect.

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Section: Introductionmentioning

confidence: 99%

An adaptive mean denoising algorithm for pepper and salt noised image

Qin

Wei

Liu

et al. 2023

Fourteenth International Conference on Graphics and Image Processing (ICGIP 2022)

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“…Unlike conventional images, in space target images, the captured images not only contain Gaussian noise, salt-and-pepper noise, Poisson noise, speckle noise, etc., but also the imaging size, shape, and gray value of space targets are highly similar to noise, and the background strength is unevenly distributed due to the scattered light. These factors cause difficulty for traditional image denoising algorithms [14][15][16][17][18][19] to be applied to the denoising of star maps, easily causing loss of target information or false targets. New requirements for image processing technology are needed to suppress or eliminate the complex background interference in star maps while preserving space target information.…”

Section: Introductionmentioning

confidence: 99%

An Image Denoising Method for a Visible Light Camera in a Complex Sky-Based Background

Zhao

Che

et al. 2023

Applied Sciences

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For space target images captured by a sky-based visible light camera, various conditions are influenced by the imaging system itself and the observation environment; these conditions include uneven image background intensity, complex noise, stray light composition, and diverse target forms. A mean wavelet transform algorithm is proposed. This algorithm initially performs mean filtering and wavelet transform filtering on the noise-containing space target images and then performs a wavelet inverse transform on the filtered results to generate the final image. The experimental results show that our algorithm has good denoising performance and can effectively maintain the image details.

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“…To our knowledge, the most commonly used denoising algorithm for salt and pepper noise is the nonlinear filter family, such as the median filter, with noise detecting step [10][11][12][13][14][15]. Empirical results reported in previous studies show that these traditional algorithms are effective for salt and pepper noise to some extent [16,22,[30][31][32][33]. Wang et al tried to apply hash retrieval and low rank methods to compute similarity and code [34][35][36][37][38][39][40][41][42].…”

Section: Introductionmentioning

confidence: 99%

A convolutional neural networks denoising approach for salt and pepper noise

Zhao

et al. 2018

Multimed Tools Appl

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The salt and pepper noise, especially the one with extremely high percentage of impulses, brings a significant challenge to image denoising. In this paper, we propose a non-local switching filter convolutional neural network denoising algorithm, named NLSF-CNN, for salt and pepper noise. As its name suggested, our NLSF-CNN consists of two steps, i.e., a NLSF processing step and a CNN training step. First, we develop a NLSF pre-processing step for noisy images using non-local information. Then, the pre-processed images are divided into patches and used for CNN training, leading to a CNN denoising model for future noisy images. We conduct a number of experiments to evaluate the effectiveness of NLSF-CNN. Experimental results show that NLSF-CNN outperforms the state-of-the-art denoising algorithms with a few training images.

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An image topic model for image denoising

Cited by 11 publications

References 17 publications

An adaptive mean denoising algorithm for pepper and salt noised image

An adaptive mean denoising algorithm for pepper and salt noised image

An Image Denoising Method for a Visible Light Camera in a Complex Sky-Based Background

A convolutional neural networks denoising approach for salt and pepper noise

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