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.

scholarly journals Adaptive Image Enhancement Using Entropy-Based Subhistogram Equalization

2018 ◽  
Vol 2018 ◽  
pp. 1-13 ◽  
Author(s):  
Liyun Zhuang ◽  
Yepeng Guan
Keyword(s):  
Image Enhancement ◽  
Dynamic Range ◽  
State Of The Art ◽  
Image Database ◽  
Input Image ◽  
Public Image ◽  
Contrast Enhanced ◽  
Quantitative Results ◽  
The Mean ◽  
Visual Assessments

A novel image enhancement approach called entropy-based adaptive subhistogram equalization (EASHE) is put forward in this paper. The proposed algorithm divides the histogram of input image into four segments based on the entropy value of the histogram, and the dynamic range of each subhistogram is adjusted. A novel algorithm to adjust the probability density function of the gray level is proposed, which can adaptively control the degree of image enhancement. Furthermore, the final contrast-enhanced image is obtained by equalizing each subhistogram independently. The proposed algorithm is compared with some state-of-the-art HE-based algorithms. The quantitative results for a public image database named CVG-UGR-Database are statistically analyzed. The quantitative and visual assessments show that the proposed algorithm outperforms most of the existing contrast-enhancement algorithms. The proposed method can make the contrast of image more effectively enhanced as well as the mean brightness and details well preserved.

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.

Advantages of using Digitized Micrographs for Serial-Section 3-D Reconstruction

1990 ◽  
Vol 48 (1) ◽  
pp. 548-549
Author(s):  
Michael Marko ◽  
ArDean Leith ◽  
Donald Parsons
Keyword(s):  
Image Enhancement ◽  
Dynamic Range ◽  
Photographic Film ◽  
Serial Sections ◽  
Low Contrast ◽  
Photographic Images ◽  
Data Editing ◽  
Contrast Structures ◽  
Digital Image Enhancement ◽  
Automatic Contouring

Digitized micrographs provide advantages in making 3-D reconstructions from serial sections. The use of image enhancement, magnification zoom, synthetic stereo, and rapid interactive review of the entire set of serial sections improves the accuracy and ease of reconstruction. We use the STERECON system for tracing contours from serial sections, storing the contour data, editing, and displaying the 3-D reconstructions. STERECON has options for stereoscopic input and display. Either digitized or conventional photographic images can be used with the system.Structures can be traced more easily in digitally-enhanced images. Digital image enhancement techniques have been extensively developed in the medical imaging field, and many of the same techniques are also useful for microscopy. Among the most difficult images to deal with photographically are those which have uneven exposure or a large variation in density. Since photographic film has a wider dynamic range than paper, it is difficult to bring out details in both light and dark areas of the image on the same print. Dodging results in loss of contrast which cannot always be recovered by using higher contrast grades of paper. Routine digital techniques can easily deal with this situation. In addition, nearly invisible, very-low-contrast structures in the image can be made distinct. Most importantly, edges of structures can be enhanced for more accurate tracing of contours. The extension of this is automatic contouring, which is used with varying degrees of success, depending on the complexity of the image.

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 Image enhancement with contrast coefficients using wavelet based image fusion

2018 ◽  
Vol 7 (2.8) ◽  
pp. 432 ◽  
Author(s):  
P Pardhasaradhi ◽  
B T PMadhav ◽  
G Lakshmi Sindhuja ◽  
K Sai Sreeram ◽  
M Parvathi ◽  
...  
Keyword(s):  
Wavelet Transform ◽  
Image Fusion ◽  
Image Enhancement ◽  
Dynamic Range ◽  
Poor Quality ◽  
Information Loss ◽  
Image Brightness ◽  
Low Contrast ◽  
Different Temperatures

The future is mainly focused on image brightness and the capacity that required storing the image. The sharp images provide better information than the blur images. To overcome from the blurriness in the image, we use image enhancement techniques. Image fusion used to overcome information loss in the image. This paper is provided with image enhancement and fusion by applying wavelet transform technique. Wavelet transform is mainly used because due to its inherent property that is they are redundant and shift invariant. It transforms the image into different scales. Image enhancement will be decided based on the levels of transformation. Low contrast results from poor resolution, lack of dynamic range, wrong settings of sensor lens during acquisition and poor quality of cameras and sensors. To avoid the information loss there is an interesting solution that is for the pictures of the same image but focused on different regions. Then using image fusion concept, all images which are captured are combined to get a single image which contains the properties of both the source images. The image entropy is composed to determine the quality of the image. The paper shows the image fusion method for both multi-resolution and images captured at different temperatures.

An Improved Algorithm Based on Retinex Theory for X-Ray Medical Image

2013 ◽  
Vol 772 ◽  
pp. 233-238
Author(s):  
Wen Bo Wang ◽  
Li Juan Zhou ◽  
Li Fei
Keyword(s):  
Image Enhancement ◽  
Medical Image ◽  
Dynamic Range ◽  
Medical Image Analysis ◽  
Practical Significance ◽  
Low Contrast ◽  
X Ray ◽  
Retinex Algorithm ◽  
Retinex Theory ◽  
Medical Image Enhancement

Retinex theory combined the elements of images and visual.This paper improved the Retinex-based medical image enhancement method, It can get better brightness by using the neural network logarithmic The S-shaped LogSig transfer function instead of the original MSR logarithm function. Based on this, the paper presents a composite LRA (LogSig Retinex Algorithm) algorithm, and analysed the shortcomings of the original Retinex algorithm applied to the X-ray medical image analysis, described the advantage of the composite LRA algorithm is better than traditional Retinex algorithm on the X-ray medical image. Experimental results show that the improved Retinex algorithm can achieve not only low-contrast medical image enhancement, but also the dynamic range compression of the image, can significantly improve the information of the medical image of the dark area. It has practical significance for clinical diagnosis.

scholarly journals An Adaptive Image Enhancement Technique by Combining Cuckoo Search and Particle Swarm Optimization Algorithm

2015 ◽  
Vol 2015 ◽  
pp. 1-12 ◽  
Author(s):  
Zhiwei Ye ◽  
Mingwei Wang ◽  
Zhengbing Hu ◽  
Wei Liu
Keyword(s):  
Particle Swarm Optimization ◽  
Image Quality ◽  
Image Enhancement ◽  
Search Algorithm ◽  
Beta Function ◽  
Particle Swarm ◽  
Cuckoo Search ◽  
Swarm Optimization ◽  
Low Contrast ◽  
Enhancement Technique

Image enhancement is an important procedure of image processing and analysis. This paper presents a new technique using a modified measure and blending of cuckoo search and particle swarm optimization (CS-PSO) for low contrast images to enhance image adaptively. In this way, contrast enhancement is obtained by global transformation of the input intensities; it employs incomplete Beta function as the transformation function and a novel criterion for measuring image quality considering three factors which are threshold, entropy value, and gray-level probability density of the image. The enhancement process is a nonlinear optimization problem with several constraints. CS-PSO is utilized to maximize the objective fitness criterion in order to enhance the contrast and detail in an image by adapting the parameters of a novel extension to a local enhancement technique. The performance of the proposed method has been compared with other existing techniques such as linear contrast stretching, histogram equalization, and evolutionary computing based image enhancement methods like backtracking search algorithm, differential search algorithm, genetic algorithm, and particle swarm optimization in terms of processing time and image quality. Experimental results demonstrate that the proposed method is robust and adaptive and exhibits the better performance than other methods involved in the paper.

scholarly journals Robust Nonparametric Distribution Transfer with Exposure Correction for Image Neural Style Transfer

Sensors ◽  
2020 ◽  
Vol 20 (18) ◽  
pp. 5232
Author(s):  
Shuai Liu ◽  
Caixia Hong ◽  
Jing He ◽  
Zhiqiang Tian
Keyword(s):  
Dynamic Range ◽  
Color Difference ◽  
Industrial Applications ◽  
Correction Algorithm ◽  
Style Transfer ◽  
Qualitative And Quantitative ◽  
Academic Literature ◽  
Nonparametric Distribution ◽  
Quantitative Results ◽  
The Given

Image neural style transfer is a process of utilizing convolutional neural networks to render a content image based on a style image. The algorithm can compute a stylized image with original content from the given content image but a new style from the given style image. Style transfer has become a hot topic both in academic literature and industrial applications. The stylized results of current existing models are not ideal because of the color difference between two input images and the inconspicuous details of content image. To solve the problems, we propose two style transfer models based on robust nonparametric distribution transfer. The first model converts the color probability density function of the content image into that of the style image before style transfer. When the color dynamic range of the content image is smaller than that of style image, this model renders more reasonable spatial structure than the existing models. Then, an adaptive detail-enhanced exposure correction algorithm is proposed for underexposed images. Based this, the second model is proposed for the style transfer of underexposed content images. It can further improve the stylized results of underexposed images. Compared with popular methods, the proposed methods achieve the satisfactory qualitative and quantitative results.

scholarly journals Weak-Light Image Enhancement Method Based on Adaptive Local Gamma Transform and Color Compensation

2021 ◽  
Vol 2021 ◽  
pp. 1-18
Author(s):  
Wencheng Wang ◽  
Xiaohui Yuan ◽  
Zhenxue Chen ◽  
XiaoJin Wu ◽  
Zairui Gao
Keyword(s):  
Image Enhancement ◽  
Dynamic Range ◽  
Color Space ◽  
Correction Method ◽  
Source Image ◽  
Low Contrast ◽  
Reflection Model ◽  
Color Compensation ◽  
Enhancement Method ◽  
Weak Light

In weak-light environments, images suffer from low contrast and the loss of details. Traditional image enhancement models are usually failure to avoid the issue of overenhancement. In this paper, a simple and novel correction method is proposed based on an adaptive local gamma transformation and color compensation, which is inspired by the illumination reflection model. Our proposed method converts the source image into YUV color space, and the Y component is estimated with a fast guided filter. The local gamma transform function is used to improve the brightness of the image by adaptively adjusting the parameters. Finally, the dynamic range of the image is optimized by a color compensation mechanism and a linear stretching strategy. By comparing with the state-of-the-art algorithms, it is demonstrated that the proposed method adaptively reduces the influence of uneven illumination to avoid overenhancement and improve the visual effect of low-light images.

Exploring Coronal Structures with SOHO

2000 ◽  
Vol 179 ◽  
pp. 403-406
Author(s):  
M. Karovska ◽  
B. Wood ◽  
J. Chen ◽  
J. Cook ◽  
R. Howard
Keyword(s):  
Solar Corona ◽  
Image Enhancement ◽  
Coronal Mass Ejections ◽  
Dynamical Evolution ◽  
Small Scale ◽  
Low Contrast ◽  
Contrast Structures ◽  
Scale Structures ◽  
Small Scale Structures ◽  
Coronal Structures

AbstractWe applied advanced image enhancement techniques to explore in detail the characteristics of the small-scale structures and/or the low contrast structures in several Coronal Mass Ejections (CMEs) observed by SOHO. We highlight here the results from our studies of the morphology and dynamical evolution of CME structures in the solar corona using two instruments on board SOHO: LASCO and EIT.

Sign in / Sign up

Export Citation Format

Share Document