An Overview and Performance Evaluation of Classification-Based Least Squares Trained Filters

Shao, Ling; Zhang, Hui; Haan, G. de

doi:10.1109/tip.2008.2002162

Cited by 73 publications

(4 citation statements)

References 19 publications

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“…The main challenge is to eliminate noise without compromising the relevant data. The methods vary greatly from local and non-local filters based on the correlation between pixels (Wiener, 1950;Brailean et al, 1995;Stockman and Shapiro, 2001;Buades et al, 2005;Shao et al, 2008) to mapping the data to other domains where patterns can be recognized (Dabov et al, 2007;Mairal et al, 2009;Luisier et al, 2010). To identify the noise, denoising tools typically require a model for the noise.…”

Section: Denoising Toolsmentioning

confidence: 99%

A deeper look into natural sciences with physics-based and data-driven measures

et al. 2021

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Summary With the development of machine learning in recent years, it is possible to glean much more information from an experimental data set to study matter. In this perspective, we discuss some state-of-the-art data-driven tools to analyze latent effects in data and explain their applicability in natural science, focusing on two recently introduced, physics-motivated computationally cheap tools—latent entropy and latent dimension. We exemplify their capabilities by applying them on several examples in the natural sciences and show that they reveal so far unobserved features such as, for example, a gradient in a magnetic measurement and a latent network of glymphatic channels from the mouse brain microscopy data. What sets these techniques apart is the relaxation of restrictive assumptions typical of many machine learning models and instead incorporating aspects that best fit the dynamical systems at hand.

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Section: Denoising Toolsmentioning

confidence: 99%

A deeper look into natural sciences with physics-based and data-driven measures

et al. 2021

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“…According to the selection of candidates, spatial filters can be categorized as local and nonlocal filters. A large number of local filtering methods are designed, such as Wiener filter [17], least mean squares filter [18], trained filter (TF) [19], bilateral filter [20], anisotropic filtering [21], steering kernel regression (SKR) [22], and kernel-based image denoising employing semiparametric regularization (KSPR) [23]. However, these methods often require the signal-to-noise ratio to be low enough.…”

Section: Introductionmentioning

confidence: 99%

A Denoising Scheme for Randomly Clustered Noise Removal in ICCD Sensing Image

Wang

Yang

et al. 2017

Sensors

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An Intensified Charge-Coupled Device (ICCD) image is captured by the ICCD image sensor in extremely low-light conditions. Its noise has two distinctive characteristics. (a) Different from the independent identically distributed (i.i.d.) noise in natural image, the noise in the ICCD sensing image is spatially clustered, which induces unexpected structure information; (b) The pattern of the clustered noise is formed randomly. In this paper, we propose a denoising scheme to remove the randomly clustered noise in the ICCD sensing image. First, we decompose the image into non-overlapped patches and classify them into flat patches and structure patches according to if real structure information is included. Then, two denoising algorithms are designed for them, respectively. For each flat patch, we simulate multiple similar patches for it in pseudo-time domain and remove its noise by averaging all the simulated patches, considering that the structure information induced by the noise varies randomly over time. For each structure patch, we design a structure-preserved sparse coding algorithm to reconstruct the real structure information. It reconstructs each patch by describing it as a weighted summation of its neighboring patches and incorporating the weights into the sparse representation of the current patch. Based on all the reconstructed patches, we generate a reconstructed image. After that, we repeat the whole process by changing relevant parameters, considering that blocking artifacts exist in a single reconstructed image. Finally, we obtain the reconstructed image by merging all the generated images into one. Experiments are conducted on an ICCD sensing image dataset, which verifies its subjective performance in removing the randomly clustered noise and preserving the real structure information in the ICCD sensing image.

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“…The images taken by different types of imaging devices usually need to be evaluated with their quality to determine whether they are suitable for specific applications. Objective image quality assessment (IQA) plays an important role in various fields including image acquisition, image super-resolution, 1,2 and image enhancement, 3 etc. IQA is also applied in applications such as image reconstruction and image retrieval.…”

Section: Introductionmentioning

confidence: 99%

Image quality assessment using deep convolutional networks

Xiang

2017

AIP Advances

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Image quality assessment metric for frame accumulated image Review of Scientific Instruments 89, 013703 (2018); https://doi.org/10.1063/1.5020715Image super resolution using deep convolutional network based on topology aggregation structure AIP Conference Proceedings 1864, 020185 (2017); https://doi.org/10.1063/1.4993002 AIP ADVANCES 7, 125324 (2017) Image quality assessment using deep convolutional networks This paper proposes a method of accurately assessing image quality without a reference image by using a deep convolutional neural network. Existing training based methods usually utilize a compact set of linear filters for learning features of images captured by different sensors to assess their quality. These methods may not be able to learn the semantic features that are intimately related with the features used in human subject assessment. Observing this drawback, this work proposes training a deep convolutional neural network (CNN) with labelled images for image quality assessment. The ReLU in the CNN allows non-linear transformations for extracting high-level image features, providing a more reliable assessment of image quality than linear filters. To enable the neural network to take images of any arbitrary size as input, the spatial pyramid pooling (SPP) is introduced connecting the top convolutional layer and the fully-connected layer. In addition, the SPP makes the CNN robust to object deformations to a certain extent. The proposed method taking an image as input carries out an end-to-end learning process, and outputs the quality of the image. It is tested on public datasets. Experimental results show that it outperforms existing methods by a large margin and can accurately assess the image quality on images taken by different sensors of varying sizes. © 2017 Author (s)

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An Overview and Performance Evaluation of Classification-Based Least Squares Trained Filters

Cited by 73 publications

References 19 publications

A deeper look into natural sciences with physics-based and data-driven measures

A deeper look into natural sciences with physics-based and data-driven measures

A Denoising Scheme for Randomly Clustered Noise Removal in ICCD Sensing Image

Image quality assessment using deep convolutional networks

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