Semi-Supervised Learning-Based Image Denoising for Big Data
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In this paper, the research of image noise reduction based on semi-supervised learning is carried out, and the neural network is used to reduce the noise of the image, so as to achieve more stable and good image display ability. Based on the convolutional neural network algorithm, the role of activation function optimization network is studied, combined with semi-supervised learning modes such as multi-feature extraction technology, to learn and extract the key features of the input image. Semi-supervised residual learning based on convolutional network is a good image denoising and denoising network model. Compared with other excellent denoising algorithms, it has very good results. At the same time, it greatly improves the image noise pollution and makes the image details clearer. At the same time, compared with other image denoising algorithms, this algorithm can show a good peak signal-to-noise ratio under various noise standard deviations. Through the research in this article, it is verified that the improved convolutional neural network denoising model and multi-feature extraction technology have strong advantages in image denoising.Keywords:
Video denoising
Supervised Learning
Feature (linguistics)
Non-Local Means
Image denoising algorithms may be the oldest in image processing. A first pre-processing step in analyzing such datasets is denoising, that is, estimating the unknown signal of interest from the available noisy data. There are several different approaches to denoise images. To remove noise several techniques and image denoising filters are used. This paper shows a comparative study and analysis of image denoising techniques relying on fuzzy filters.
Video denoising
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Non-Local Means
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Non-Local Means
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One of the bottleneck in medical imaging is due to low Signal-to-Noise Ratio(SNR) which requires long and repeated acquisition of the same subject to reduce noise and blur. To obtain a high SNR image without lengthy repeated scans, post processing of data such as denoising plays a critical role. Bilateral and Nonlocal means (NLm) filtering are commonly used procedures for medical image denoising. In this paper we propose a new thresholding scheme to wavelet and contour let based denoising by introducing a scaling factor to universal threshold. Also we propose the aforementioned novel contour let thresholding scheme as a preprocessing step on bilateral filtering and NLm denoising. Simulation results show that the proposed single entity comprising the novel preprocessing step and bilateral or NLm denoising is superior in terms of PSNR and perceptual quality compared to Bilateral filtering or NLm denoising used alone.
Video denoising
Non-Local Means
Step detection
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In this paper, a new algorithm for color video denoising is proposed. This is a joint implementation of thresholding based DWT & DAMMW filtering algorithms. It provides a better improvement in the parameters of denoising and removes any type of noise easily. Moreover, proposed algorithm consists of a new wavelet threshold denoising function with an improved threshold value. It will not only retain the advantages of hard and soft denoising functions but also overcomes the disadvantages of hard denoising function and soft denoising function. Simulation result ensures the effectiveness of proposed work.
Video denoising
Non-Local Means
Step detection
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Digital images are frequently degraded by Gaussian noise while capturing photos. This paper proposes a rapid and high accurate Gaussian noise removal method by applying the learned linear filter used in RAISR for super-resolution. The denoising methods are classified into local, nonlocal methods and deep-learning-based methods. The conventional local processing has a problem that high-frequency components of the original image are lost while reducing the noise. The nonlocal and deep-learning-based methods achieve higher denoising performance but take a long time for training and implementation. To solve these problems, we apply a super-resolution method to the local denoising method as post-processing because it can efficiently recover the high-frequency components. The super-resolution method uses a learned linear filter according to the feature of patches. The novelty of this paper is that the same processing as super-resolution is incorporated into denoising. The proposed algorithm is a rapid local denoising method and can achieve comparable performance to the high-accurate nonlocal denoising methods. Experimental results show that our proposed method provides accurate denoising performance with a low computational cost compared to nonlocal processing like BM3D.
Non-Local Means
Video denoising
Step detection
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Bilateral filter and Total variation image denoising are widely used in image denoising. In low noisy level, bilateral filtering is better than TV denoising for it reveals better SNR and sharper edges. However, in high noisy level, TV denoising outperforms bilateral filtering in terms of SNR and more details of non edges. It is very difficult to perform denoising of a very noisy image for the resulted image rarely improves its SNR comparing to the original noisy one. Even though Total variation image denoising could be used for a very noisy image, the resulted SNR still needs some improvement. In this research, the K-means-based Bilateral-TV denoising (K-BiTV) approach using pixel-wise bilateral filtering and TV denoising has been derived based on the gradient magnitude calculation of the guideline map using K-means clusters. The denoising result of K-BiTV was depended on the level of noise density and the appropriate cluster. The experimental result showed that comparing to the conventional TV denoising and bilateral filter, K-BiTV gave the higher SNRs for some images with higher level of noise density.
Video denoising
Non-Local Means
Total variation denoising
Step detection
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The denoising of Gaussian additive white noise is a classical problem in signal and image processing. In this paper, we classify the most important wavelet denoising methods into different categories and give a brief overview of each method classified. In general, the recently developed block matching and 3D filtering (BM3D) algorithm performs much better than other existing methods published in the literature. We recommend using this method for image denoising because it is currently one of the state-of-the-art denoising methods. The non-local means method and the optimal spatial adaptation (OSA) method are also very successful methods in image denoising.
Video denoising
Non-Local Means
Total variation denoising
Step detection
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Image denoising is an important pre-processing step in image analysis. Various denoising algorithms, such as BM3D, PCD and K-SVD, obtain remarkable effects. Recently a deep denoising auto-encoder has been proposed and shown excellent performance compared to conventional image denoising algorithms. In this paper, we study the statistical features of restored image residuals produced by Denoising Auto-encoders and propose an improved training loss function for Denoising Auto-encoders based on Method noise and entropy maximization principle, with residual statistics as constraint conditions. We compare it with conventional denoising algorithms including original Denoising Auto-encoders, BM3D, total variation (TV) minimization, and non-local mean (NLM) algorithms. Experiments indicate that the Improved Denoising Auto-encoders introduce less non-existent artifacts and are more robustness than other state-of-the-art denoising methods in both PSNR and SSIM indexes, especially under low SNR.
Video denoising
Non-Local Means
Robustness
Total variation denoising
Minification
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Image denoising technology is one of the forelands in the field of computer graphic and computer vision. Non-local means method is one of the great performing methods which arouse tremendous research. In this paper, an improved weighted non-local means algorithm for image denoising is proposed. The non-local means denoising method replaces each pixel by the weighted average of pixels with the surrounding neighborhoods. The proposed method evaluates on testing images with various levels noise. Experimental results show that the algorithm improves the denoising performance.
Non-Local Means
Video denoising
Step detection
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Citations (15)