scholarly journals A Review on the Underwater Image Restoration and Enhancement Methods

2021 ◽  
Vol 14 ◽  
pp. 1-13
Author(s):  
Siaw Lang Wong ◽  
Raveendran Paramesran ◽  
Seng Huat Ong
Keyword(s):  
Light Scattering ◽  
Image Restoration ◽  
Quality Assessment ◽  
Light Absorption ◽  
State Of The Art ◽  
Assessment Methods ◽  
Blue Colour ◽  
Underwater Image ◽  
Color Cast

Light scattering, as well as light absorption in the water, often cause underwater images to be hazy, poorly contrasted, and dominated by either green or blue colour cast. In this paper, we review some of the state-of-the-art approaches in which specifically designed to enhance the quality of the acquired images. These approaches are able to eliminate the color cast and haziness on the images as well as to improve the image colourfulness and contrast. The characteristics of each of the developed approaches are highlighted, and their performances are evaluated both subjectively and objectively by the quality assessment methods.

Fusion of Underwater Image Enhancement and Restoration

2019 ◽  
Vol 34 (03) ◽  
pp. 2054007 ◽  
Author(s):  
Rajni Sethi ◽  
Sreedevi Indu
Keyword(s):  
Image Enhancement ◽  
State Of The Art ◽  
Input Image ◽  
Laplacian Pyramid ◽  
Novel Technique ◽  
Underwater Image ◽  
Single Input ◽  
Color Cast ◽  
Better Than

Optical properties of water distort the quality of underwater images. Underwater images are characterized by poor contrast, color cast, noise and haze. These images need to be pre-processed so as to get some information. In this paper, a novel technique named Fusion of Underwater Image Enhancement and Restoration (FUIER) has been proposed which enhances as well as restores underwater images with a target to act on all major issues in underwater images, i.e. color cast removal, contrast enhancement and dehazing. It generates two versions of the single input image and these two versions are fused using Laplacian pyramid-based fusion to get the enhanced image. The proposed method works efficiently for all types of underwater images captured in different conditions (turbidity, depth, salinity, etc.). Results obtained using the proposed method are better than those for state-of-the-art methods.

scholarly journals Underwater Image Restoration via Non-Convex Non-Smooth Variation and Thermal Exchange Optimization

2021 ◽  
Vol 9 (6) ◽  
pp. 570
Author(s):  
Qingliang Jiao ◽  
Ming Liu ◽  
Pengyu Li ◽  
Liquan Dong ◽  
Mei Hui ◽  
...  
Keyword(s):  
Image Restoration ◽  
Qualitative And Quantitative Analysis ◽  
Formation Model ◽  
High Quality ◽  
Thermal Exchange ◽  
Qualitative And Quantitative ◽  
Smooth Variation ◽  
Underwater Image ◽  
Dark Channel

The quality of underwater images is an important problem for resource detection. However, the light scattering and plankton in water can impact the quality of underwater images. In this paper, a novel underwater image restoration based on non-convex, non-smooth variation and thermal exchange optimization is proposed. Firstly, the underwater dark channel prior is used to estimate the rough transmission map. Secondly, the rough transmission map is refined by the proposed adaptive non-convex non-smooth variation. Then, Thermal Exchange Optimization is applied to compensate for the red channel of underwater images. Finally, the restored image can be estimated via the image formation model. The results show that the proposed algorithm can output high-quality images, according to qualitative and quantitative analysis.

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.

scholarly journals DLNR-SIQA: Deep Learning-Based No-Reference Stitched Image Quality Assessment

Sensors ◽  
2020 ◽  
Vol 20 (22) ◽  
pp. 6457
Author(s):  
Hayat Ullah ◽  
Muhammad Irfan ◽  
Kyungjin Han ◽  
Jong Weon Lee
Keyword(s):  
Deep Learning ◽  
Image Quality ◽  
Quality Assessment ◽  
Image Quality Assessment ◽  
State Of The Art ◽  
Reference Image ◽  
Primary Concern ◽  
Panoramic Images ◽  
Stitching Errors

Due to recent advancements in virtual reality (VR) and augmented reality (AR), the demand for high quality immersive contents is a primary concern for production companies and consumers. Similarly, the topical record-breaking performance of deep learning in various domains of artificial intelligence has extended the attention of researchers to contribute to different fields of computer vision. To ensure the quality of immersive media contents using these advanced deep learning technologies, several learning based Stitched Image Quality Assessment methods have been proposed with reasonable performances. However, these methods are unable to localize, segment, and extract the stitching errors in panoramic images. Further, these methods used computationally complex procedures for quality assessment of panoramic images. With these motivations, in this paper, we propose a novel three-fold Deep Learning based No-Reference Stitched Image Quality Assessment (DLNR-SIQA) approach to evaluate the quality of immersive contents. In the first fold, we fined-tuned the state-of-the-art Mask R-CNN (Regional Convolutional Neural Network) on manually annotated various stitching error-based cropped images from the two publicly available datasets. In the second fold, we segment and localize various stitching errors present in the immersive contents. Finally, based on the distorted regions present in the immersive contents, we measured the overall quality of the stitched images. Unlike existing methods that only measure the quality of the images using deep features, our proposed method can efficiently segment and localize stitching errors and estimate the image quality by investigating segmented regions. We also carried out extensive qualitative and quantitative comparison with full reference image quality assessment (FR-IQA) and no reference image quality assessment (NR-IQA) on two publicly available datasets, where the proposed system outperformed the existing state-of-the-art techniques.

A sustainable quality assessment model for the information delivery in E-learning systems

2018 ◽  
Vol 46 (1) ◽  
pp. 1-25 ◽  
Author(s):  
Shahid Farid ◽  
Rodina Ahmad ◽  
Mujahid Alam ◽  
Atif Akbar ◽  
Victor Chang
Keyword(s):  
Quality Assessment ◽  
State Of The Art ◽  
Statistical Tests ◽  
Information Delivery ◽  
Learning Systems ◽  
Learning System ◽  
Assessment Model ◽  
Content Type ◽  
E Learning

Purpose The purpose of this study is to propose a sustainable quality assessment approach (model) for the e-learning systems keeping software perspective under consideration. E-learning is becoming mainstream due to its accessibility, state-of-the-art learning, training ease and cost effectiveness. However, the poor quality of e-learning systems is one of the major causes of several failures reported. Moreover, this arena lacks well-defined quality assessment measures. Hence, it is quite difficult to measure the overall quality of an e-learning system effectively. Design/methodology/approach A pragmatic mixed-model philosophy was adopted for this study. A systematic literature review was performed to identify existing e-learning quality models and frameworks. Semi-structured interviews were conducted with e-learning experts following empirical investigations to identify the crucial quality characteristics of e-learning systems. Various statistical tests like principal component analysis, logistic regression, chi-square and analysis of means were applied to analyze the empirical data. These led to an adequate set of quality indicators that can be used by higher education institutions to assure the quality of e-learning systems. Findings A sustainable quality assessment model for the information delivery in e-learning systems in software perspective has been proposed by exploring the state-of-the-art quality assessment/evaluation models and frameworks proposed for the e-learning systems. The proposed model can be used to assess and improve the process of information discovery and delivery of e-learning. Originality/value The results obtained led to conclude that very limited attention is given to the quality of e-learning tools despite the importance of quality and its effect on e-learning system adoption and promotion. Moreover, the identified models and frameworks do not adequately address quality of e-learning systems from a software perspective.

scholarly journals Digital Elevation Model Quality Assessment Methods: A Critical Review

2020 ◽  
Vol 12 (21) ◽  
pp. 3522
Author(s):  
Laurent Polidori ◽  
Mhamad El Hage
Keyword(s):  
Quality Assessment ◽  
Assessment Methods ◽  
Internal Quality ◽  
Surface Model ◽  
Model Quality ◽  
Topographic Features ◽  
Digital Elevation ◽  
Elevation Model ◽  
User Perspectives

Digital elevation models (DEMs) are widely used in geoscience. The quality of a DEM is a primary requirement for many applications and is affected during the different processing steps, from the collection of elevations to the interpolation implemented for resampling, and it is locally influenced by the landcover and the terrain slope. The quality must meet the user’s requirements, which only make sense if the nominal terrain and the relevant resolution have been explicitly specified. The aim of this article is to review the main quality assessment methods, which may be separated into two approaches, namely, with or without reference data, called external and internal quality assessment, respectively. The errors and artifacts are described. The methods to detect and quantify them are reviewed and discussed. Different product levels are considered, i.e., from point cloud to grid surface model and to derived topographic features, as well as the case of global DEMs. Finally, the issue of DEM quality is considered from the producer and user perspectives.

Design of modern tools for digital output raster scanning

2018 ◽  
Vol 2 (21) ◽  
pp. 157-163
Author(s):  
Oleg Yushchyk ◽  
Bogdana Havrysh ◽  
Oleksandr Tymchenko ◽  
Karolina Szturo
Keyword(s):  
Quality Control ◽  
Quality Assessment ◽  
Production Process ◽  
Assessment Methods ◽  
Digital Output ◽  
Raster Scanning ◽  
Technological Processes ◽  
Technological Effectiveness ◽  
And Control

Quality control at all stages of the polygraphic process will allow to establish a feedback between technological processes of printing. The validity of the selected criteria, technological effectiveness and objectivity of the quality assessment methods will allow to organise technically accurate modelling of the processes as well as to promptly interfere with the production process if necessary. The tendency of the development of technologies and control tools as well as computer expansion are the reasons why the objectivity and impartiality of the assessment become the main criteria for the choice of method for quality assessment of the polygrafic product. Methodological purpose of the quality control is to make the polygrafic process technologically driven and stable, and the quality of the received print - more predictable.

A novel point cloud quality assessment metric based on perceptual color distance patterns

2021 ◽  
Vol 2021 (9) ◽  
pp. 256-1-256-11
Author(s):  
Rafael Diniz ◽  
Pedro Garcia Freitas ◽  
Mylène Farias
Keyword(s):  
Quality Assessment ◽  
Point Cloud ◽  
State Of The Art ◽  
Perceptual Quality ◽  
Great Effort ◽  
Robust Performance ◽  
Wide Range ◽  
Objective Quality ◽  
Novel Method

In recent years, PCs have become very popular for a wide range of applications, such as immersive virtual reality scenarios. As a consequence, in the last couple of years, there has been a great effort to develop novel acquisition, representation, compression, and transmission solutions for PC contents in the research community. In particular, the development of objective quality assessment methods that are able to predict the perceptual quality of PCs. In this paper, we present an effective novel method for assessing the quality of PCs, which is based on descriptors that extract perceptual color distance-based texture information of PC contents, called Perceptual Color Distance Patterns (PCDP). In this framework, the statistics of the extracted information are used to model the PC visual quality. Experimental results show that the proposed framework exhibit good and robust performance when compared with several state-of-the-art point cloud quality assessment (PCQA) methods.

scholarly journals A Combined Full-Reference Image Quality Assessment Method Based on Convolutional Activation Maps

Algorithms ◽  
2020 ◽  
Vol 13 (12) ◽  
pp. 313
Author(s):  
Domonkos Varga
Keyword(s):  
Image Quality ◽  
Quality Assessment ◽  
Image Quality Assessment ◽  
Feature Vector ◽  
State Of The Art ◽  
Prediction Performance ◽  
Perceptual Quality ◽  
Reference Image ◽  
Full Reference

The goal of full-reference image quality assessment (FR-IQA) is to predict the perceptual quality of an image as perceived by human observers using its pristine (distortion free) reference counterpart. In this study, we explore a novel, combined approach which predicts the perceptual quality of a distorted image by compiling a feature vector from convolutional activation maps. More specifically, a reference-distorted image pair is run through a pretrained convolutional neural network and the activation maps are compared with a traditional image similarity metric. Subsequently, the resulting feature vector is mapped onto perceptual quality scores with the help of a trained support vector regressor. A detailed parameter study is also presented in which the design choices of the proposed method is explained. Furthermore, we study the relationship between the amount of training images and the prediction performance. Specifically, it is demonstrated that the proposed method can be trained with a small amount of data to reach high prediction performance. Our best proposal—called ActMapFeat—is compared to the state-of-the-art on six publicly available benchmark IQA databases, such as KADID-10k, TID2013, TID2008, MDID, CSIQ, and VCL-FER. Specifically, our method is able to significantly outperform the state-of-the-art on these benchmark databases.

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