scholarly journals An Adaptive Boosting Algorithm for Image Denoising

2019 ◽  
Vol 2019 ◽  
pp. 1-14
Author(s):  
Zhuang Fang ◽  
Xuming Yi ◽  
Liming Tang
Keyword(s):  
Image Denoising ◽  
Noise Level ◽  
Signal To Noise Ratio ◽  
Visual Quality ◽  
Estimation Algorithm ◽  
Noisy Image ◽  
Adaptive Boosting ◽  
Noise Level Estimation ◽  
Parameter Configuration ◽  
Boosting Algorithm

Image denoising is an important problem in many fields of image processing. Boosting algorithm attracts extensive attention in recent years, which provides a general framework by strengthening the original noisy image. In such framework, many classical existing denoising algorithms can improve the denoising performance. However, the boosting step is fixed or nonadaptive; i.e., the noise level in iteration steps is set to be a constant. In this work, we propose a noise level estimation algorithm by combining the overestimation and underestimation results. Based on this, we further propose an adaptive boosting algorithm that excludes intricate parameter configuration. Moreover, we prove the convergence of the proposed algorithm. Experimental results that are obtained in this paper demonstrate the effectiveness of the proposed adaptive boosting algorithm. In addition, compared with the classical boosting algorithm, the proposed algorithm can get better performance in terms of visual quality and peak signal-to-noise ratio (PSNR).

scholarly journals Minimum Array Elements for Resolution of Several Direction of Arrival Estimation Methods in Various Noise-Level Environments

2018 ◽  
Vol 2 ◽  
pp. 87-94
Author(s):  
Ismail El Ouargui ◽  
Said Safi ◽  
Miloud Frikel
Keyword(s):  
Gaussian Noise ◽  
Noise Level ◽  
Signal To Noise Ratio ◽  
Additive White Gaussian Noise ◽  
Direction Of Arrival ◽  
Estimation Algorithm ◽  
Doa Estimation ◽  
Estimation Methods ◽  
Minimum Number ◽  
Informative Signals

The resolution of a Direction of Arrival (DOA) estimation algorithm is determined based on its capability to resolve two closely spaced signals. In this paper, authors present and discuss the minimum number of array elements needed for the resolution of nearby sources in several DOA estimation methods. In the real world, the informative signals are corrupted by Additive White Gaussian Noise (AWGN). Thus, a higher signal-to-noise ratio (SNR) offers a better resolution. Therefore, we show the performance of each method by applying the algorithms in different noise level environments.

scholarly journals An Efficient Image Denoising Method based on Bilateral filter Model and Neighshrink SURE

2019 ◽  
Vol 8 (3) ◽  
pp. 8470-8475
Keyword(s):  
Image Quality ◽  
Image Denoising ◽  
Signal To Noise Ratio ◽  
Research Work ◽  
Visual Quality ◽  
Bilateral Filter ◽  
Standard Test ◽  
Systematic Research ◽  
Denoising Method ◽  
Filter Model

In all the instances of image acquisition, transmission and storage, the unwanted noise gets into the information content of the image and thereby introduces an unpleasant visual quality to the observer. So the field of image processing has produced a lot of image denoising algorithms and techniques to improve the visual quality of the image. Since noise cannot be reduced to zero practically, the need for faithful and efficient denoising techniques to produce almost noiseless images demands a systematic research work in the field of denoising methods. The denoising process using a bilateral filter even though produces improvement in the image quality, it does not show consistency when the noise level is high and also the peak signal to noise ratio (PSNR) and Image quality Index (IQI) do not show any improvement. This paper proposes an improved algorithm that incorporates the function of bilateral filter model and wavelet thresholding using Neighshrink SURE method. The results show significant improvement in both PSNR and IQI values with respect to the four standard test images under various noise conditions.

Secrets of image denoising cuisine

Acta Numerica ◽  
2012 ◽  
Vol 21 ◽  
pp. 475-576 ◽  
Author(s):  
M. Lebrun ◽  
M. Colom ◽  
A. Buades ◽  
J. M. Morel
Keyword(s):  
Stochastic Process ◽  
Numerical Methods ◽  
Image Denoising ◽  
Digital Images ◽  
Signal To Noise Ratio ◽  
Noise Model ◽  
Noisy Image ◽  
Image Model ◽  
Signal To Noise ◽  
Photon Count

Digital images are matrices of equally spaced pixels, each containing a photon count. This photon count is a stochastic process due to the quantum nature of light. It follows that all images are noisy. Ever since digital images have existed, numerical methods have been proposed to improve the signal-to-noise ratio. Such ‘denoising’ methods require a noise model and an image model. It is relatively easy to obtain a noise model. As will be explained in the present paper, it is even possible to estimate it from a single noisy image.

scholarly journals Comparative analysis of different image denoising methods in spatial and transform domain

2021 ◽  
Author(s):  
Zeeshan Ahmad
Keyword(s):  
Comparative Analysis ◽  
Image Denoising ◽  
Gaussian Noise ◽  
Signal To Noise Ratio ◽  
Visual Quality ◽  
Structural Similarity ◽  
Noise Model ◽  
Transform Domain ◽  
Noise Levels ◽  
Extract Information

Digital Images are the best source for humans to see, visualize, think, extract information and make conclusions. However during the acquisition of images, noise superimposes on the images and reduces the information and detail of the images. In order to restore the details of the images, noise must be reduced from the images. This requirement places the image denoising amongst the fundamental and challenging fields of computer vision and image processing. In this project six fundamental techniques / algorithms of image denoising in spatial and transform domain are presented and their comparative analysis is also carried out. The noise model used in this project is Additive Gaussian noise. The algorithms are simulated on Matlab and experimental results are shown at different noise levels. The performance of each image denoising technique is measured in terms of Peak Signal to Noise Ratio (PSNR) , Mean Structural Similarity (SSIM) Metrics and visual quality. It is observed that the transform domain techniques used in this project achieved better results as compared to spatial domain techniques

scholarly journals A New Image Denoising Method by Combining WT with ICA

2015 ◽  
Vol 2015 ◽  
pp. 1-10 ◽  
Author(s):  
Chengzhi Ruan ◽  
Dean Zhao ◽  
Weikuan Jia ◽  
Chen Chen ◽  
Yu Chen ◽  
...  
Keyword(s):  
Image Denoising ◽  
Signal To Noise Ratio ◽  
Median Filtering ◽  
Noisy Image ◽  
Original Image ◽  
Signal To Noise ◽  
Denoising Method ◽  
Noise Image ◽  
Solid Foundation ◽  
Lower Noise

In order to improve the image denoising ability, the wavelet transform (WT) and independent component analysis (ICA) are both introduced into image denoising in this paper. Although these two algorithms have their own advantages in image denoising, they are unable to reduce noises completely, which makes it difficult to achieve ideal effect. Therefore, a new image denoising method is proposed based on the combination of WT with ICA (WT-ICA). For verifying the WT-ICA denoising method, we adopt four image denoising methods for comparison: median filtering (MF), wavelet soft thresholding (WST), ICA, and WT-ICA. From the experimental results, it is shown that WT-ICA can significantly reduce noises and get lower-noise image. Moreover, the average of WT-ICA denoising image’s peak signal to noise ratio (PSNR) is improved by 20.54% compared with noisy image and 11.68% compared with the classical WST denoising image, which demonstrates its advantage. From the performance of texture and edge detection, denoising image by WT-ICA is closer to the original image. Therefore, the new method has its unique advantage in image denoising, which lays a solid foundation for the realization of further image processing task.

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