The amount of noise included in a hyperspectral image limits its application and has a negative impact on hyperspectral image classification, unmixing, target detection, and so on. In hyperspectral images, because the noise intensity in different bands is different, to better suppress the noise in the high-noise-intensity bands and preserve the detailed information in the low-noise-intensity ba...

In this paper, a denoising approach, which exploits patchredundancy for removing Gaussian noise from RGB color images is described. Both geometrical and photometrical similarity of image patches have to be considered for learning the parameters of this Patch-based Locally Optimal Weiner(PLOW) filer. K-means clustering,with LARK(Locally Adaptive Regression Kernel) features, is used to identify t...

Wavelet packet analysis is a mathematical transformation that can be used to post-process images, for example, to remove image noise ("denoising"). At a very low signal-to-noise ratio (SNR <5), standard magnitude magnetic resonance images have skewed Rician noise statistics that degrade denoising performance. Since the quadrature images have approximately Gaussian noise, it was postulated that ...

Denoising and compression is the best crucial technique to overcome this issue. SPHIT is mostly refined type of the algorithm of EZW and it is better algorithms for image compression that makes a set of bit stream from which we can get best renovate images. Through these algorithms, the values of highest PSNR of different type of images compression ration can be acquired. The denoised image per...

The denoising method based on empirical mode decomposition (EMD) can be broadly divided into: IMF extraction method and IMF threshold approach. Aiming to the problems of how to select IMFs in extraction method and the processing of the selected IMFs, a threshold denoising method based on EMD is put forward. In this method, the standard of IMF selection in energy viewpoint is offered, and the IM...

In this paper, we propose a Correlation-Maximization denoising filter which utilizes periodicity information to remove additive noise in bird calls. We also developed a statistically-based noise robust bird-call classification system which uses the denoising filter as a frontend. Enhanced bird calls which are the output of the denoising filter are used for feature extraction. Gaussian Mixture M...

Signal denoising can not only enhance the signal to noise ratio (SNR) but also reduce the effect of noise. In order to satisfy the requirements of real-time signal denoising, an improved semisoft shrinkage real-time denoising method based on lifting wavelet transform was proposed. The moving data window technology realizes the real-time wavelet denoising, which employs wavelet transform based o...

In this paper we report an interesting observation pertaining to Denoising based on the optimization of image processing chains. Although often a goal in itself, Denoising is usually performed in order to minimize the detrimental effects of noise in the subsequent stages of an algorithm. Typically, Denoising is carried out as an early pre-processing stage before other core functions are applied...

In electron tomographic reconstructions of biological specimens the information about their structure is not directly accessible since most of the signal is buried in noise. An interpretation of the images using surface and volume rendering techniques is difficult due to the noise sensitivity of rendering algorithms. We explore the use of various multiscale representations for denoising 2D and ...

Digital image processing remains a challenging domain of programming. All digital images contain some degree of noise. Often times this noise is introduced by the camera when a picture is taken. Image denoising algorithms attempt to remove this noise from the image. In this paper the method for image denoising based on the nonlocal means (NL-means) algorithm has been implemented and results hav...