Self-Similarity Measure for Assessment of Image Visual Quality

2011 ◽  
pp. 459-470 ◽  
Author(s):  
Nikolay Ponomarenko ◽  
Lina Jin ◽  
Vladimir Lukin ◽  
Karen Egiazarian
Keyword(s):  
Similarity Measure ◽  
Visual Quality ◽  
Self Similarity

scholarly journals Superpixel Spectral Unmixing for Hyperspectral Image Superresolution Using a Coupled Encoder-Decoder Network

2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
Shao-lei Zhang ◽  
Guang-yuan Fu ◽  
Hong-qiao Wang ◽  
Yu-qing Zhao
Keyword(s):  
Hyperspectral Image ◽  
Visual Quality ◽  
Spectral Unmixing ◽  
The Other ◽  
Hyperspectral Images ◽  
Low Rank ◽  
Spectral Information ◽  
Multispectral Images ◽  
Self Similarity ◽  
Rank Constraint

In this paper, we propose a novel hyperspectral image superresolution method based on superpixel spectral unmixing using a coupled encoder-decoder network. The hyperspectral image and multispectral images are fused to generate high-resolution hyperspectral images through the spectral unmixing framework with low-rank constraint. Specifically, the endmember and abundance information is extracted via a coupled encoder-decoder network integrating the priori for unmixing. The coupled network consists of two encoders and one shared decoder, where spectral information is preserved through the encoder. The multispectral image is clustered into superpixels to explore self-similarity, and then, the superpixels are unmixed to obtain an abundance matrix. By imposing a low-rank constraint on the abundance matrix, we further improve the superresolution performance. Experiments on the CAVE and Harvard datasets indicate that our superresolution method outperforms the other compared methods in terms of quantitative evaluation and visual quality.

scholarly journals Selfish: discovery of differential chromatin interactions via a self-similarity measure

2019 ◽  
Vol 35 (14) ◽  
pp. i145-i153 ◽  
Author(s):  
Abbas Roayaei Ardakany ◽  
Ferhat Ay ◽  
Stefano Lonardi
Keyword(s):  
Comparative Analysis ◽  
Similarity Measure ◽  
Fundamental Problem ◽  
Three Dimensional ◽  
Biological Significance ◽  
Real Data ◽  
Dimensional Structure ◽  
Self Similarity ◽  
Contact Maps ◽  
Chromatin Interactions

AbstractMotivationHigh-throughput conformation capture experiments, such as Hi-C provide genome-wide maps of chromatin interactions, enabling life scientists to investigate the role of the three-dimensional structure of genomes in gene regulation and other essential cellular functions. A fundamental problem in the analysis of Hi-C data is how to compare two contact maps derived from Hi-C experiments. Detecting similarities and differences between contact maps are critical in evaluating the reproducibility of replicate experiments and for identifying differential genomic regions with biological significance. Due to the complexity of chromatin conformations and the presence of technology-driven and sequence-specific biases, the comparative analysis of Hi-C data is analytically and computationally challenging.ResultsWe present a novel method called Selfish for the comparative analysis of Hi-C data that takes advantage of the structural self-similarity in contact maps. We define a novel self-similarity measure to design algorithms for (i) measuring reproducibility for Hi-C replicate experiments and (ii) finding differential chromatin interactions between two contact maps. Extensive experimental results on simulated and real data show that Selfish is more accurate and robust than state-of-the-art methods.Availability and implementationhttps://github.com/ucrbioinfo/Selfish

scholarly journals Efficient Sky Dehazing by Atmospheric Light Fusion

Sensors ◽  
2020 ◽  
Vol 20 (17) ◽  
pp. 4893 ◽  
Author(s):  
Jaouad Hajjami ◽  
Thibault Napoléon ◽  
Ayman Alfalou
Keyword(s):  
Iterative Algorithm ◽  
Similarity Measure ◽  
Visual Quality ◽  
Relevant Information ◽  
New Method ◽  
Gaussian Filters ◽  
Gradient Optimization ◽  
Optimization Function ◽  
Random Variability ◽  
Atmospheric Light

In this article, we present a new method of dehazing based on the Koschmieder model, which aims to restore an image that has been affected by haze. The difficulty is to improve the estimation of the transmission and the atmospheric light that generally suffer from the nonhomogeneity and the random variability of the environment. The keypoint is to enhance the dehazing of very bright regions of the image in order to improve the treatment of the sky that is often overestimated or underestimated compared to the rest of the scene. The approach proposed in this paper is based on two main contributions: 1. an L0 gradient optimization function weighted by a set of Gaussian filters and based on an iterative algorithm for optimization convergence. Unlike the existing methods using a single value of the atmospheric light for the whole image, our method uses a set of values neighboring an initial estimated value. The fusion is then applied based on Laplacian and Gaussian pyramids to combine all the relevant information from the set of images constructed from atmospheric lights and improves the contrast to recover the colors of the sky without any artifacts. Finally, the results are validated by three criteria: an autocorrelation score (ZNCC), a similarity measure (SSIM) and a visual criterion. The experiments carried out on two datasets show that our approach allows a better dehazing of the images with higher SSIM and ZNCC measurements but also with better visual quality.

scholarly journals Non-Rigid Multi-Modal 3D Medical Image Registration Based on Foveated Modality Independent Neighborhood Descriptor

Sensors ◽  
2019 ◽  
Vol 19 (21) ◽  
pp. 4675 ◽  
Author(s):  
Feng Yang ◽  
Mingyue Ding ◽  
Xuming Zhang
Keyword(s):  
Image Registration ◽  
Similarity Measure ◽  
Energy Function ◽  
Medical Image ◽  
Medical Image Registration ◽  
Self Similarity ◽  
Registration Accuracy ◽  
Registration Method ◽  
Rigid Deformation ◽  
Transformation Parameters

The non-rigid multi-modal three-dimensional (3D) medical image registration is highly challenging due to the difficulty in the construction of similarity measure and the solution of non-rigid transformation parameters. A novel structural representation based registration method is proposed to address these problems. Firstly, an improved modality independent neighborhood descriptor (MIND) that is based on the foveated nonlocal self-similarity is designed for the effective structural representations of 3D medical images to transform multi-modal image registration into mono-modal one. The sum of absolute differences between structural representations is computed as the similarity measure. Subsequently, the foveated MIND based spatial constraint is introduced into the Markov random field (MRF) optimization to reduce the number of transformation parameters and restrict the calculation of the energy function in the image region involving non-rigid deformation. Finally, the accurate and efficient 3D medical image registration is realized by minimizing the similarity measure based MRF energy function. Extensive experiments on 3D positron emission tomography (PET), computed tomography (CT), T1, T2, and PD weighted magnetic resonance (MR) images with synthetic deformation demonstrate that the proposed method has higher computational efficiency and registration accuracy in terms of target registration error (TRE) than the registration methods that are based on the hybrid L-BFGS-B and cat swarm optimization (HLCSO), the sum of squared differences on entropy images, the MIND, and the self-similarity context (SSC) descriptor, except that it provides slightly bigger TRE than the HLCSO for CT-PET image registration. Experiments on real MR and ultrasound images with unknown deformation have also be done to demonstrate the practicality and superiority of the proposed method.

Rotation invariant similarity measure for non-local self-similarity based image denoising

2015 ◽  
Author(s):  
Chenglin Zuo ◽  
Ljubomir Jovanov ◽  
Hiep Quang Luong ◽  
Bart Goossens ◽  
Wilfried Philips ◽  
...  
Keyword(s):  
Image Denoising ◽  
Similarity Measure ◽  
Self Similarity ◽  
Rotation Invariant ◽  
Non Local

scholarly journals Selfish: Discovery of Differential Chromatin Interactions via a Self-Similarity Measure

2019 ◽  
Author(s):  
Abbas Roayaei Ardakany ◽  
Ferhat Ay ◽  
Stefano Lonardi
Keyword(s):  
Comparative Analysis ◽  
Similarity Measure ◽  
Fundamental Problem ◽  
Three Dimensional ◽  
Biological Significance ◽  
Real Data ◽  
Dimensional Structure ◽  
Self Similarity ◽  
Contact Maps ◽  
Chromatin Interactions

AbstractMotivationHigh-throughput conformation capture experiments such as Hi-C provide genome-wide maps of chromatin interactions, enabling life scientists to investigate the role of the three-dimensional structure of genomes in gene regulation and other essential cellular functions. A fundamental problem in the analysis of Hi-C data is how to compare two contact maps derived from Hi-C experiments. Detecting similarities and differences between contact maps is critical in evaluating the reproducibility of replicate experiments and identifying differential genomic regions with biological significance. Due to the complexity of chromatin conformations and the presence of technology-driven and sequence-specific biases, the comparative analysis of Hi-C data is analytically and computationally challenging.ResultsWe present a novel method called Selfish for the comparative analysis of Hi-C data that takes advantage of the structural self-similarity in contact maps. We define a novel self-similarity measure to design algorithms for (i) measuring reproducibility for Hi-C replicate experiments and (ii) finding differential chromatin interactions between two contact maps. Extensive experimental results on simulated and real data show that Selfish is more accurate and robust than state-of-the-art methods.Availabilityhttps://github.com/ucrbioinfo/[email protected] and [email protected]

Sign in / Sign up

Export Citation Format

Share Document