Removal Of Blocking Artifacts From JPEG-Compressed Images Using a Neural Network

被引:0
作者
Marsh, Ronald [1 ]
Amin, Md Nurul [1 ]
机构
[1] Univ North Dakota, Sch Elect Engn & Comp Sci, Grand Forks, ND 58202 USA
来源
2020 IEEE INTERNATIONAL CONFERENCE ON ELECTRO INFORMATION TECHNOLOGY (EIT) | 2020年
关键词
JPEG compression; Neural networks; CODED IMAGES; REDUCTION; ENHANCEMENT; DEBLOCKING; ALGORITHM;
D O I
10.1109/eit48999.2020.9208336
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
The goal of this research was to develop a neural network that will improve the quality of JPEG compressed images, irrespective of compression level. After reviewing related articles, published papers, and previous works on developing a computationally efficient algorithm for reducing the blockiness and Gibbs oscillation artifacts in JPEG compressed images, we decided to integrate a neural network into a previously developed technique. For this approach, the Alphablend filter [35] was used to post process JPEG compressed images to reduce noise and artifacts. The Alphablend result was further improved upon by the application of a trained neural network. We compare our results with various other published works using post compression filtering methods.
引用
收藏
页码:255 / 258
页数:4
相关论文
共 50 条
[41]   Identification of Noisy Poultry Portion Images Using a Neural Network [J].
Khashman, Adnan ;
Asiksoy, Gulsum Y. .
PROCEEDINGS OF THE 9TH WSEAS INTERNATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE, KNOWLEDGE ENGINEERING AND DATA BASES, 2010, :77-+
[42]   Segmentation of MR and CT images by using a quantiser neural network [J].
Dokur, Z ;
Ölmez, T .
NEURAL COMPUTING & APPLICATIONS, 2003, 11 (3-4) :168-177
[43]   Segmentation of MR and CT Images by Using a Quantiser Neural Network [J].
Zümray Dokur ;
Tamer Ölmez .
Neural Computing & Applications, 2003, 11 :168-177
[44]   Cancer diagnostics using neural network sorting of processed images [J].
Wyman, CL ;
Schreeder, M ;
Grundy, W ;
Kinser, J .
APPLICATIONS AND SCIENCE OF ARTIFICIAL NEURAL NETWORKS II, 1996, 2760 :324-332
[45]   Photoplethysmography Motion Artifacts Removal Based on Signal-Noise Interaction Modeling Utilizing Envelope Filtering and Time-Delay Neural Network [J].
Xu, Ke ;
Jiang, Xinyu ;
Chen, Wei .
IEEE SENSORS JOURNAL, 2020, 20 (07) :3732-3744
[46]   Effective Hotspot Removal System Using Neural Network Predictor [J].
Oh, Sangyoon ;
Kang, Mun-Young ;
Kang, Sanggil .
INTELLIGENT INFORMATION AND DATABASE SYSTEMS (ACIIDS 2013), PT II, 2013, 7803 :478-488
[47]   Mixed Gaussian-impulse noise reduction from images using convolutional neural network [J].
Islam, Mohammad Tariqul ;
Rahman, S. M. Mahbubur ;
Ahmad, M. Omair ;
Swamy, M. N. S. .
SIGNAL PROCESSING-IMAGE COMMUNICATION, 2018, 68 :26-41
[48]   A Neural Network Based Kidney Segmentation from MR Images [J].
Goceri, Numan ;
Goceri, Evgin .
2015 IEEE 14TH INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND APPLICATIONS (ICMLA), 2015, :1195-1198
[49]   Two-Stage Convolutional Neural Network for Ship and Spill Detection Using SLAR Images [J].
Nieto-Hidalgo, Mario ;
Gallego, Antonio-Javier ;
Gil, Pablo ;
Pertusa, Antonio .
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2018, 56 (09) :5217-5230
[50]   Noise removal in electroencephalogram signals using an artificial neural network based on the simultaneous perturbation method [J].
Mateo, J. ;
Torres, A. M. ;
Garcia, M. A. ;
Santos, J. L. .
NEURAL COMPUTING & APPLICATIONS, 2016, 27 (07) :1941-1957