scholarly journals FAOD-Net: A Fast AOD-Net for Dehazing Single Image

2020 ◽  
Vol 2020 ◽  
pp. 1-11 ◽  
Author(s):  
Wen Qian ◽  
Chao Zhou ◽  
Dengyin Zhang
Keyword(s):  
Quality Evaluation ◽  
Deep Neural Networks ◽  
Reference Image ◽  
Context Information ◽  
Global Information ◽  
Single Image ◽  
Training Set ◽  
Test Set ◽  
Full Reference ◽  
Proposed Model

In this paper, we present an extremely computation-efficient model called FAOD-Net for dehazing single image. FAOD-Net is based on a streamlined architecture that uses depthwise separable convolutions to build lightweight deep neural networks. Moreover, the pyramid pooling module is added in FAOD-Net to aggregate the context information of different regions of the image, thereby improving the ability of the network model to obtain the global information of the foggy image. To get the best FAOD-Net, we use the RESIDE training set to train our proposed model. In addition, we have carried out extensive experiments on the RESIDE test set. We use full-reference and no-reference image quality evaluation indicators to measure the effect of dehazing. Experimental results show that the proposed algorithm has satisfactory results in terms of defogging quality and speed.

Single Image Dehazing by Predicting Atmospheric Scattering Parameters

2020 ◽  
Vol 2020 (1) ◽  
pp. 74-77
Author(s):  
Simone Bianco ◽  
Luigi Celona ◽  
Flavio Piccoli
Keyword(s):  
Reference Image ◽  
Single Image ◽  
Scattering Parameters ◽  
Image Dehazing ◽  
Atmospheric Scattering ◽  
Local Structures ◽  
Full Reference ◽  
Reconstruction Quality ◽  
Single Image Dehazing

In this work we propose a method for single image dehazing that exploits a physical model to recover the haze-free image by estimating the atmospheric scattering parameters. Cycle consistency is used to further improve the reconstruction quality of local structures and objects in the scene as well. Experimental results on four real and synthetic hazy image datasets show the effectiveness of the proposed method in terms of two commonly used full-reference image quality metrics.

Chinese Image Captioning via Fuzzy Attention-based DenseNet-BiLSTM

2021 ◽  
Vol 17 (1s) ◽  
pp. 1-18 ◽  
Author(s):  
Huimin Lu ◽  
Rui Yang ◽  
Zhenrong Deng ◽  
Yonglin Zhang ◽  
Guangwei Gao ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Contextual Information ◽  
Image Features ◽  
Context Information ◽  
Global Information ◽  
Image Description ◽  
Image Captioning ◽  
Image Content ◽  
Single Feature ◽  
Proposed Model

Chinese image description generation tasks usually have some challenges, such as single-feature extraction, lack of global information, and lack of detailed description of the image content. To address these limitations, we propose a fuzzy attention-based DenseNet-BiLSTM Chinese image captioning method in this article. In the proposed method, we first improve the densely connected network to extract features of the image at different scales and to enhance the model’s ability to capture the weak features. At the same time, a bidirectional LSTM is used as the decoder to enhance the use of context information. The introduction of an improved fuzzy attention mechanism effectively improves the problem of correspondence between image features and contextual information. We conduct experiments on the AI Challenger dataset to evaluate the performance of the model. The results show that compared with other models, our proposed model achieves higher scores in objective quantitative evaluation indicators, including BLEU , BLEU , METEOR, ROUGEl, and CIDEr. The generated description sentence can accurately express the image content.

scholarly journals Local Indicators of Spatial Autocorrelation (LISA): Application to Blind Noise-Based Perceptual Quality Metric Index for Magnetic Resonance Images

2019 ◽  
Vol 5 (1) ◽  
pp. 20 ◽  
Author(s):  
Michael Osadebey ◽  
Marius Pedersen ◽  
Douglas Arnold ◽  
Katrina Wendel-Mitoraj
Keyword(s):  
Performance Evaluation ◽  
Spatial Autocorrelation ◽  
Quality Evaluation ◽  
Magnetic Resonance Images ◽  
Quality Metrics ◽  
Quality Score ◽  
Perceptual Quality ◽  
Reference Image ◽  
Full Reference ◽  
Reference Methods

Noise-based quality evaluation of MRI images is highly desired in noise-dominant environments. Current noise-based MRI quality evaluation methods have drawbacks which limit their effective performance. Traditional full-reference methods such as SNR and most of the model-based techniques cannot provide perceptual quality metrics required for accurate diagnosis, treatment and monitoring of diseases. Although techniques based on the Moran coefficients are perceptual quality metrics, they are full-reference methods and will be ineffective in applications where the reference image is not available. Furthermore, the predicted quality scores are difficult to interpret because their quality indices are not standardized. In this paper, we propose a new no-reference perceptual quality evaluation method for grayscale images such as MRI images. Our approach is formulated to mimic how humans perceive an image. It transforms noise level into a standardized perceptual quality score. Global Moran statistics is combined with local indicators of spatial autocorrelation in the form of local Moran statistics. Quality score is predicted from perceptually weighted combination of clustered and random pixels. Performance evaluation, comparative performance evaluation and validation by human observers, shows that the proposed method will be a useful tool in the evaluation of retrospectively acquired MRI images and the evaluation of noise reduction algorithms.

scholarly journals A Regression-Based Family of Measures for Full-Reference Image Quality Assessment

2016 ◽  
Vol 16 (6) ◽  
pp. 316-325 ◽  
Author(s):  
Mariusz Oszust
Keyword(s):  
Image Quality ◽  
Visual Perception ◽  
Quality Assessment ◽  
Quality Evaluation ◽  
Image Quality Assessment ◽  
Human Subjects ◽  
Reference Image ◽  
Human Visual Perception ◽  
Full Reference ◽  
Highly Correlated

Abstract The advances in the development of imaging devices resulted in the need of an automatic quality evaluation of displayed visual content in a way that is consistent with human visual perception. In this paper, an approach to full-reference image quality assessment (IQA) is proposed, in which several IQA measures, representing different approaches to modelling human visual perception, are efficiently combined in order to produce objective quality evaluation of examined images, which is highly correlated with evaluation provided by human subjects. In the paper, an optimisation problem of selection of several IQA measures for creating a regression-based IQA hybrid measure, or a multimeasure, is defined and solved using a genetic algorithm. Experimental evaluation on four largest IQA benchmarks reveals that the multimeasures obtained using the proposed approach outperform state-of-the-art full-reference IQA techniques, including other recently developed fusion approaches.

scholarly journals Deep Neural Networks for No-Reference and Full-Reference Image Quality Assessment

2018 ◽  
Vol 27 (1) ◽  
pp. 206-219 ◽  
Author(s):  
Sebastian Bosse ◽  
Dominique Maniry ◽  
Klaus-Robert Muller ◽  
Thomas Wiegand ◽  
Wojciech Samek
Keyword(s):  
Neural Networks ◽  
Image Quality ◽  
Quality Assessment ◽  
Deep Neural Networks ◽  
Image Quality Assessment ◽  
Reference Image ◽  
Full Reference

scholarly journals Adaptation of Full-Reference Image Quality Assessment Methods for Automatic Visual Evaluation of the Surface Quality of 3D Prints

2019 ◽  
Vol 25 (5) ◽  
pp. 57-62 ◽  
Author(s):  
Krzysztof Okarma ◽  
Jaroslaw Fastowicz
Keyword(s):  
Additive Manufacturing ◽  
Image Quality ◽  
Quality Assessment ◽  
Surface Quality ◽  
Quality Evaluation ◽  
Image Quality Assessment ◽  
Assessment Methods ◽  
Reference Image ◽  
Visual Evaluation ◽  
Full Reference

Automatic visual quality assessment of the 3D printed surfaces is currently one of the most demanding challenges in additive manufacturing. Regardless of the applications of the computer vision for the 3D printing process monitoring purposes, a reliable surface quality evaluation during manufacturing may introduce brand new possibilities. The detection of some distortions and their automatic evaluation can be helpful when deciding to stop the process to save time, energy, and filament. In some cases, some further corrections can also be made for relatively small distortions. Since many general-purpose image quality assessment methods have been proposed in recent years, their applications for the quality evaluation in the additive manufacturing are investigated. As most of the metrics are full-reference and require the availability of the original perfect quality image, their direct application is not possible. Therefore, their adaptation is described in the paper together with experimental verification of classification results obtained using various metrics.

PREDIKSI KUALITAS AIR SUNGAI CILIWUNG DENGAN MENGGUNAKAN ALGORITMA POHON KEPUTUSAN

2021 ◽  
Vol 12 (2) ◽  
Author(s):  
Mohammad Haekal ◽  
Henki Bayu Seta ◽  
Mayanda Mega Santoni
Keyword(s):  
Data Mining ◽  
Decision Tree ◽  
Cross Validation ◽  
Online Monitoring ◽  
Training Set ◽  
Microsoft Excel ◽  
Test Set

Untuk memprediksi kualitas air sungai Ciliwung, telah dilakukan pengolahan data-data hasil pemantauan secara Online Monitoring dengan menggunakan Metode Data Mining. Pada metode ini, pertama-tama data-data hasil pemantauan dibuat dalam bentuk tabel Microsoft Excel, kemudian diolah menjadi bentuk Pohon Keputusan yang disebut Algoritma Pohon Keputusan (Decision Tree) mengunakan aplikasi WEKA. Metode Pohon Keputusan dipilih karena lebih sederhana, mudah dipahami dan mempunyai tingkat akurasi yang sangat tinggi. Jumlah data hasil pemantauan kualitas air sungai Ciliwung yang diolah sebanyak 5.476 data. Hasil klarifikasi dengan Pohon Keputusan, dari 5.476 data ini diperoleh jumlah data yang mengindikasikan sungai Ciliwung Tidak Tercemar sebanyak 1.059 data atau sebesar 19,3242%, dan yang mengindikasikan Tercemar sebanyak 4.417 data atau 80,6758%. Selanjutnya data-data hasil pemantauan ini dievaluasi menggunakan 4 Opsi Tes (Test Option) yaitu dengan Use Training Set, Supplied Test Set, Cross-Validation folds 10, dan Percentage Split 66%. Hasil evaluasi dengan 4 opsi tes yang digunakan ini, semuanya menunjukkan tingkat akurasi yang sangat tinggi, yaitu diatas 99%. Dari data-data hasil peneltian ini dapat diprediksi bahwa sungai Ciliwung terindikasi sebagai sungai tercemar bila mereferensi kepada Peraturan Pemerintah Republik Indonesia nomor 82 tahun 2001 dan diketahui pula bahwa penggunaan aplikasi WEKA dengan Algoritma Pohon Keputusan untuk mengolah data-data hasil pemantauan dengan mengambil tiga parameter (pH, DO dan Nitrat) adalah sangat akuran dan tepat. Kata Kunci : Kualitas air sungai, Data Mining, Algoritma Pohon Keputusan, Aplikasi WEKA.

scholarly journals QSPR modelling of the octanol/water partition coefficient of organometallic substances by optimal SMILES-based descriptors

2009 ◽  
Vol 7 (4) ◽  
pp. 846-856 ◽  
Author(s):  
Andrey Toropov ◽  
Alla Toropova ◽  
Emilio Benfenati
Keyword(s):  
Partition Coefficient ◽  
Organometallic Compounds ◽  
Applicability Domain ◽  
Training Set ◽  
Input Line ◽  
Test Set ◽  
Water Partition Coefficient ◽  
Definition Of

AbstractUsually, QSPR is not used to model organometallic compounds. We have modeled the octanol/water partition coefficient for organometallic compounds of Na, K, Ca, Cu, Fe, Zn, Ni, As, and Hg by optimal descriptors calculated with simplified molecular input line entry system (SMILES) notations. The best model is characterized by the following statistics: n=54, r2=0.9807, s=0.677, F=2636 (training set); n=26, r2=0.9693, s=0.969, F=759 (test set). Empirical criteria for the definition of the applicability domain for these models are discussed.

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