Image enhancement to leverage the 3D morphological reconstruction of single-cell neurons

2021 ◽  
Author(s):  
Shuxia Guo ◽  
Xuan Zhao ◽  
Shengdian Jiang ◽  
Liya Ding ◽  
Hanchuan Peng
Keyword(s):  
Image Quality ◽  
Image Enhancement ◽  
Data Storage ◽  
Dynamic Range ◽  
Source Code ◽  
Supplementary Information ◽  
High Signal ◽  
Morphological Reconstruction ◽  
Neuron Reconstruction ◽  
Background Contrast

Abstract Motivation To digitally reconstruct the 3D neuron morphologies has long been a major bottleneck in neuroscience. One of the obstacles to automate the procedure is the low signal-background contrast and the large dynamic range of signal and background both within and across images. Results We developed a pipeline to enhance the neurite signal and to suppress the background, with the goal of high signal-background contrast and better within- and between image homogeneity. The performance of the image enhancement was quantitatively verified according to the different figures of merit benchmarking the image quality. Additionally, the method could improve the neuron reconstruction in approximately 1/3 of the cases, with very few cases of degrading the reconstruction. This significantly outperformed three other approaches of image enhancement. Moreover, the compression rate was increased 5 times by average comparing the enhanced to the raw image. All results demonstrated the potential of the proposed method in leveraging the neuroscience providing better 3D morphological reconstruction and lower cost of data storage and transfer. Availability The study is conducted based on the Vaa3D platform and python 3.7.9. The Vaa3D platform is available on the GitHub (https://github.com/Vaa3D). The source code of the proposed image enhancement as a Vaa3D plugin, the source code to benchmark the image quality, and the example image blocks are available under the repository of vaa3d_tools/hackathon/SGuo/imPreProcess. The original fMost images of mouse brains can be found at the BICCN’s Brain Image Library (BIL) (https://www.brainimagelibrary.org). Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals UCAN: A Learning-based Model to Enhance Poorly Exposed Images

2020 ◽  
Author(s):  
Lucas R. V. Messias ◽  
Cristiano R. Steffens ◽  
Paulo L. J. Drews-Jr ◽  
Silvia S. C. Botelho
Keyword(s):  
Image Quality ◽  
Image Enhancement ◽  
Dynamic Range ◽  
Low Contrast ◽  
Qualitative And Quantitative ◽  
Fast Learning ◽  
Proposed Model ◽  
Quantitative Results ◽  
Critical Process ◽  
Poor Lighting

Image enhancement is a critical process in imagebased systems. In these systems, image quality is a crucial factor to achieve a good performance. Scenes with a dynamic range above the capability of the camera or poor lighting are challenging conditions, which usually result in low contrast images, and, with that, we can have the underexposure and/or overexposure problem. In this work, our aim is to restore illexposed images. For this purpose, we present UCAN, a small and fast learning-based model capable to restore and enhance poorly exposed images. The obtained results are evaluated using image quality indicators which show that the proposed network is able to improve images damaged by real and simulated exposure. Qualitative and quantitative results show that the proposed model outperforms the existing models for this objective.

Perceptual Quality Assessment of Low-light Image Enhancement

2021 ◽  
Vol 17 (4) ◽  
pp. 1-24
Author(s):  
Guangtao Zhai ◽  
Wei Sun ◽  
Xiongkuo Min ◽  
Jiantao Zhou
Keyword(s):  
Image Quality ◽  
Quality Assessment ◽  
Image Enhancement ◽  
Dynamic Range ◽  
State Of The Art ◽  
Perceptual Quality ◽  
Low Light ◽  
Light Image ◽  
Light Enhancement

Low-light image enhancement algorithms (LIEA) can light up images captured in dark or back-lighting conditions. However, LIEA may introduce various distortions such as structure damage, color shift, and noise into the enhanced images. Despite various LIEAs proposed in the literature, few efforts have been made to study the quality evaluation of low-light enhancement. In this article, we make one of the first attempts to investigate the quality assessment problem of low-light image enhancement. To facilitate the study of objective image quality assessment (IQA), we first build a large-scale low-light image enhancement quality (LIEQ) database. The LIEQ database includes 1,000 light-enhanced images, which are generated from 100 low-light images using 10 LIEAs. Rather than evaluating the quality of light-enhanced images directly, which is more difficult, we propose to use the multi-exposure fused (MEF) image and stack-based high dynamic range (HDR) image as a reference and evaluate the quality of low-light enhancement following a full-reference (FR) quality assessment routine. We observe that distortions introduced in low-light enhancement are significantly different from distortions considered in traditional image IQA databases that are well-studied, and the current state-of-the-art FR IQA models are also not suitable for evaluating their quality. Therefore, we propose a new FR low-light image enhancement quality assessment (LIEQA) index by evaluating the image quality from four aspects: luminance enhancement, color rendition, noise evaluation, and structure preserving, which have captured the most key aspects of low-light enhancement. Experimental results on the LIEQ database show that the proposed LIEQA index outperforms the state-of-the-art FR IQA models. LIEQA can act as an evaluator for various low-light enhancement algorithms and systems. To the best of our knowledge, this article is the first of its kind comprehensive low-light image enhancement quality assessment study.

Colour Image Enhancement using Perceptual Saturation and Vividness

2019 ◽  
Vol 2019 (1) ◽  
pp. 243-246
Author(s):  
Muhammad Safdar ◽  
Noémie Pozzera ◽  
Jon Yngve Hardeberg
Keyword(s):  
Image Quality ◽  
Image Enhancement ◽  
Test Performance ◽  
Appearance Model ◽  
Colour Space ◽  
Categorical Judgment ◽  
Saturation Scale ◽  
Colour Image ◽  
Colour Appearance ◽  
Future Test

A perceptual study was conducted to enhance colour image quality in terms of naturalness and preference using perceptual scales of saturation and vividness. Saturation scale has been extensively used for this purpose while vividness has been little used. We used perceptual scales of a recently developed colour appearance model based on Jzazbz uniform colour space. A two-fold aim of the study was (i) to test performance of recently developed perceptual scales of saturation and vividness compared with previously used hypothetical models and (ii) to compare performance and chose one of saturation and vividness scales for colour image enhancement in future. Test images were first transformed to Jzazbz colour space and their saturation and vividness were then decreased or increased to obtain 6 different variants of the image. Categorical judgment method was used to judge preference and naturalness of different variants of the test images and results are reported.

A practical approach on non-regular sampling and universal demosaicing of raw image sensor data

2020 ◽  
Vol 2020 (1) ◽  
pp. 91-95
Author(s):  
Philipp Backes ◽  
Jan Fröhlich
Keyword(s):  
Image Quality ◽  
Source Code ◽  
Image Sensor ◽  
Sensor Data ◽  
Color Filter ◽  
Lowpass Filter ◽  
Sampling Errors ◽  
Regular Sampling ◽  
Single Sensor ◽  
Similar Image Quality

Non-regular sampling is a well-known method to avoid aliasing in digital images. However, the vast majority of single sensor cameras use regular organized color filter arrays (CFAs), that require an optical-lowpass filter (OLPF) and sophisticated demosaicing algorithms to suppress sampling errors. In this paper a variety of non-regular sampling patterns are evaluated, and a new universal demosaicing algorithm based on the frequency selective reconstruction is presented. By simulating such sensors it is shown that images acquired with non-regular CFAs and no OLPF can lead to a similar image quality compared to their filtered and regular sampled counterparts. The MATLAB source code and results are available at: http://github. com/PhilippBackes/dFSR

KEC: unique sequence search by K-mer exclusion

2021 ◽  
Author(s):  
Pavel Beran ◽  
Dagmar Stehlíková ◽  
Stephen P Cohen ◽  
Vladislav Čurn
Keyword(s):  
Amino Acid ◽  
Nucleic Acid ◽  
Source Code ◽  
Unique Sequence ◽  
Supplementary Information ◽  
Supplementary Data ◽  
Laptop Computers ◽  
Sequence Search ◽  
Target Sequences ◽  
Cross Reference

Abstract Summary Searching for amino acid or nucleic acid sequences unique to one organism may be challenging depending on size of the available datasets. K-mer elimination by cross-reference (KEC) allows users to quickly and easily find unique sequences by providing target and non-target sequences. Due to its speed, it can be used for datasets of genomic size and can be run on desktop or laptop computers with modest specifications. Availability and implementation KEC is freely available for non-commercial purposes. Source code and executable binary files compiled for Linux, Mac and Windows can be downloaded from https://github.com/berybox/KEC. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals An Underwater Image Enhancement Algorithm Based on Generative Adversarial Network and Natural Image Quality Evaluation Index

2021 ◽  
Vol 9 (7) ◽  
pp. 691
Author(s):  
Kai Hu ◽  
Yanwen Zhang ◽  
Chenghang Weng ◽  
Pengsheng Wang ◽  
Zhiliang Deng ◽  
...  
Keyword(s):  
Image Quality ◽  
Image Enhancement ◽  
Quality Evaluation ◽  
High Efficiency ◽  
Natural Image ◽  
High Quality ◽  
Generative Adversarial Network ◽  
Image Quality Evaluation ◽  
Adversarial Network ◽  
Underwater Image

When underwater vehicles work, underwater images are often absorbed by light and scattered and diffused by floating objects, which leads to the degradation of underwater images. The generative adversarial network (GAN) is widely used in underwater image enhancement tasks because it can complete image-style conversions with high efficiency and high quality. Although the GAN converts low-quality underwater images into high-quality underwater images (truth images), the dataset of truth images also affects high-quality underwater images. However, an underwater truth image lacks underwater image enhancement, which leads to a poor effect of the generated image. Thus, this paper proposes to add the natural image quality evaluation (NIQE) index to the GAN to provide generated images with higher contrast and make them more in line with the perception of the human eye, and at the same time, grant generated images a better effect than the truth images set by the existing dataset. In this paper, several groups of experiments are compared, and through the subjective evaluation and objective evaluation indicators, it is verified that the enhanced image of this algorithm is better than the truth image set by the existing dataset.

scholarly journals BioCommons: a robust java library for RNA structural bioinformatics

2021 ◽  
Author(s):  
Tomasz Zok
Keyword(s):  
Source Code ◽  
Structural Bioinformatics ◽  
Supplementary Information ◽  
Supplementary Data ◽  
Bioinformatic Tools ◽  
Data Formats ◽  
Central Repository ◽  
Diverse Data ◽  
2D And 3D ◽  
Java Library

Abstract Motivation Biomolecular structures come in multiple representations and diverse data formats. Their incompatibility with the requirements of data analysis programs significantly hinders the analytics and the creation of new structure-oriented bioinformatic tools. Therefore, the need for robust libraries of data processing functions is still growing. Results BioCommons is an open-source, Java library for structural bioinformatics. It contains many functions working with the 2D and 3D structures of biomolecules, with a particular emphasis on RNA. Availability and implementation The library is available in Maven Central Repository and its source code is hosted on GitHub: https://github.com/tzok/BioCommons Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Ab initiostructure determination of nanocrystals of organic pharmaceutical compounds by electron diffraction at room temperature using a Timepix quantum area direct electron detector

2016 ◽  
Vol 72 (2) ◽  
pp. 236-242 ◽  
Author(s):  
E. van Genderen ◽  
M. T. B. Clabbers ◽  
P. P. Das ◽  
A. Stewart ◽  
I. Nederlof ◽  
...  
Keyword(s):  
Electron Diffraction ◽  
Room Temperature ◽  
Dynamic Range ◽  
Signal To Noise Ratio ◽  
Three Dimensional ◽  
Direct Methods ◽  
Liquid Nitrogen Temperature ◽  
High Dynamic Range ◽  
Electron Diffraction Data ◽  
High Signal

Until recently, structure determination by transmission electron microscopy of beam-sensitive three-dimensional nanocrystals required electron diffraction tomography data collection at liquid-nitrogen temperature, in order to reduce radiation damage. Here it is shown that the novel Timepix detector combines a high dynamic range with a very high signal-to-noise ratio and single-electron sensitivity, enablingab initiophasing of beam-sensitive organic compounds. Low-dose electron diffraction data (∼0.013 e− Å−2 s−1) were collected at room temperature with the rotation method. It was ascertained that the data were of sufficient quality for structure solution using direct methods using software developed for X-ray crystallography (XDS,SHELX) and for electron crystallography (ADT3D/PETS,SIR2014).

A Modified Multi Scale Retinex with Color Restoration Algorithm for Automatic Enhancement of Landsat-5 Remote Sensing Image

2011 ◽  
Vol 341-342 ◽  
pp. 893-897
Author(s):  
Gui Zhou Wang ◽  
Guo Jin He
Keyword(s):  
Image Enhancement ◽  
Dynamic Range ◽  
Color Constancy ◽  
Human Perception ◽  
Histogram Equalization ◽  
Image Processing Algorithm ◽  
Multi Scale ◽  
Dynamic Range Compression ◽  
Restoration Algorithm ◽  
Color Restoration

The retinex is a human perception based image processing algorithm which provides color constancy and dynamic range compression. The multi scale retinex with color restoration (MSRCR) has shown itself to be a very versatile automatic image enhancement algorithm that simultaneously provides dynamic range compression, color constancy, and color rendition. But the MSRCR results suffer from lower global brightness and partial color distortion. In order to improve the MSRCR method, this paper presents a modified MSRCR algorithm to Landsat-5 image enhancement considering percent liner stretch and histogram adjustment. Finally, the effect of modified MSRCR method on Landsat-5 image enhancement is analyzed and the comparison with other color adjustment methods such as gamma correction and histogram equalization is reported in the experimental results.

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