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Join the community, search results, scikit-image: image processing in python.

1 code implementation • 23 Jul 2014

scikit-image is an image processing library that implements algorithms and utilities for use in research, education and industry applications.

Loss Functions for Neural Networks for Image Processing

2 code implementations • 28 Nov 2015

Neural networks are becoming central in several areas of computer vision and image processing and different architectures have been proposed to solve specific problems.

Image Processing GNN: Breaking Rigidity in Super-Resolution

1 code implementation • CVPR 2024

Alternatively we leverage the flexibility of graphs and propose the Image Processing GNN (IPG) model to break the rigidity that dominates previous SR methods.

Picasso: A Modular Framework for Visualizing the Learning Process of Neural Network Image Classifiers

1 code implementation • 16 May 2017

Picasso is a free open-source (Eclipse Public License) web application written in Python for rendering standard visualizations useful for analyzing convolutional neural networks.

MAXIM: Multi-Axis MLP for Image Processing

1 code implementation • CVPR 2022

In this work, we present a multi-axis MLP based architecture called MAXIM, that can serve as an efficient and flexible general-purpose vision backbone for image processing tasks.

Instruct-IPT: All-in-One Image Processing Transformer via Weight Modulation

1 code implementation • 30 Jun 2024

We have conducted experiments on Instruct-IPT to demonstrate the effectiveness of our method on manifold tasks, and we have effectively extended our method to diffusion denoisers as well.

Fast Image Processing with Fully-Convolutional Networks

2 code implementations • ICCV 2017

Our approach uses a fully-convolutional network that is trained on input-output pairs that demonstrate the operator's action.

Simple Image Signal Processing using Global Context Guidance

1 code implementation • 17 Apr 2024

First, we propose a novel module that can be integrated into any neural ISP to capture the global context information from the full RAW images.

Pre-Trained Image Processing Transformer

6 code implementations • CVPR 2021

To maximally excavate the capability of transformer, we present to utilize the well-known ImageNet benchmark for generating a large amount of corrupted image pairs.

In Defense of Classical Image Processing: Fast Depth Completion on the CPU

2 code implementations • 31 Jan 2018

With the rise of data driven deep neural networks as a realization of universal function approximators, most research on computer vision problems has moved away from hand crafted classical image processing algorithms.

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EDITORIAL article

Editorial: current trends in image processing and pattern recognition.

• PAMI Research Lab, Computer Science, University of South Dakota, Vermillion, SD, United States

Editorial on the Research Topic Current Trends in Image Processing and Pattern Recognition

Technological advancements in computing multiple opportunities in a wide variety of fields that range from document analysis ( Santosh, 2018 ), biomedical and healthcare informatics ( Santosh et al., 2019 ; Santosh et al., 2021 ; Santosh and Gaur, 2021 ; Santosh and Joshi, 2021 ), and biometrics to intelligent language processing. These applications primarily leverage AI tools and/or techniques, where topics such as image processing, signal and pattern recognition, machine learning and computer vision are considered.

With this theme, we opened a call for papers on Current Trends in Image Processing & Pattern Recognition that exactly followed third International Conference on Recent Trends in Image Processing & Pattern Recognition (RTIP2R), 2020 (URL: http://rtip2r-conference.org ). Our call was not limited to RTIP2R 2020, it was open to all. Altogether, 12 papers were submitted and seven of them were accepted for publication.

In Deshpande et al. , authors addressed the use of global fingerprint features (e.g., ridge flow, frequency, and other interest/key points) for matching. With Convolution Neural Network (CNN) matching model, which they called “Combination of Nearest-Neighbor Arrangement Indexing (CNNAI),” on datasets: FVC2004 and NIST SD27, their highest rank-I identification rate of 84.5% was achieved. Authors claimed that their results can be compared with the state-of-the-art algorithms and their approach was robust to rotation and scale. Similarly, in Deshpande et al. , using the exact same datasets, exact same set of authors addressed the importance of minutiae extraction and matching by taking into low quality latent fingerprint images. Their minutiae extraction technique showed remarkable improvement in their results. As claimed by the authors, their results were comparable to state-of-the-art systems.

In Gornale et al. , authors extracted distinguishing features that were geometrically distorted or transformed by taking Hu’s Invariant Moments into account. With this, authors focused on early detection and gradation of Knee Osteoarthritis, and they claimed that their results were validated by ortho surgeons and rheumatologists.

In Tamilmathi and Chithra , authors introduced a new deep learned quantization-based coding for 3D airborne LiDAR point cloud image. In their experimental results, authors showed that their model compressed an image into constant 16-bits of data and decompressed with approximately 160 dB of PSNR value, 174.46 s execution time with 0.6 s execution speed per instruction. Authors claimed that their method can be compared with previous algorithms/techniques in case we consider the following factors: space and time.

In Tamilmathi and Chithra , authors carefully inspected possible signs of plant leaf diseases. They employed the concept of feature learning and observed the correlation and/or similarity between symptoms that are related to diseases, so their disease identification is possible.

In Das Chagas Silva Araujo et al. , authors proposed a benchmark environment to compare multiple algorithms when one needs to deal with depth reconstruction from two-event based sensors. In their evaluation, a stereo matching algorithm was implemented, and multiple experiments were done with multiple camera settings as well as parameters. Authors claimed that this work could be considered as a benchmark when we consider robust evaluation of the multitude of new techniques under the scope of event-based stereo vision.

In Steffen et al. ; Gornale et al. , authors employed handwritten signature to better understand the behavioral biometric trait for document authentication/verification, such letters, contracts, and wills. They used handcrafter features such as LBP and HOG to extract features from 4,790 signatures so shallow learning can efficiently be applied. Using k-NN, decision tree and support vector machine classifiers, they reported promising performance.

Author Contributions

The author confirms being the sole contributor of this work and has approved it for publication.

Conflict of Interest

The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s Note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Santosh, KC, Antani, S., Guru, D. S., and Dey, N. (2019). Medical Imaging Artificial Intelligence, Image Recognition, and Machine Learning Techniques . United States: CRC Press . ISBN: 9780429029417. doi:10.1201/9780429029417

CrossRef Full Text | Google Scholar

Santosh, KC, Das, N., and Ghosh, S. (2021). Deep Learning Models for Medical Imaging, Primers in Biomedical Imaging Devices and Systems . United States: Elsevier . eBook ISBN: 9780128236505.

Google Scholar

Santosh, KC (2018). Document Image Analysis - Current Trends and Challenges in Graphics Recognition . United States: Springer . ISBN 978-981-13-2338-6. doi:10.1007/978-981-13-2339-3

Santosh, KC, and Gaur, L. (2021). Artificial Intelligence and Machine Learning in Public Healthcare: Opportunities and Societal Impact . Spain: SpringerBriefs in Computational Intelligence Series . ISBN: 978-981-16-6768-8. doi:10.1007/978-981-16-6768-8

Santosh, KC, and Joshi, A. (2021). COVID-19: Prediction, Decision-Making, and its Impacts, Book Series in Lecture Notes on Data Engineering and Communications Technologies . United States: Springer Nature . ISBN: 978-981-15-9682-7. doi:10.1007/978-981-15-9682-7

Keywords: artificial intelligence, computer vision, machine learning, image processing, signal processing, pattern recocgnition

Citation: Santosh KC (2021) Editorial: Current Trends in Image Processing and Pattern Recognition. Front. Robot. AI 8:785075. doi: 10.3389/frobt.2021.785075

Received: 28 September 2021; Accepted: 06 October 2021; Published: 09 December 2021.

Edited and reviewed by:

Copyright © 2021 Santosh. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: KC Santosh, [email protected]

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

Journal of Real-Time Image Processing

Although there are many journals addressing the subject of image processing, the Journal of Real-Time Image Processing (JRTIP) is the only one that is solely dedicated to the real-time aspects of image and video processing such as computational complexity, frame processing rate of operation, real-time hardware implementation, real-time software optimization, etc.

• The journal is dedicated to the real-time aspects of image and video processing, bridging the gap between theory and practice.
• Covers real-time image processing systems and algorithms for various applications.
• Presents practical and real-time architectures for image processing systems.
• Provides tools, simulation and modeling for real-time image processing algorithm implementations.
• Serves researchers, engineers, and industrial professionals dealing with real-time image and video processing systems.

The median time indicated below is computed over all the submitted manuscripts, including the ones that are not put into the review pipeline at the onset of the review process. A typical review time for manuscripts that go through the review pipeline is about 100 days.

• Nasser Kehtarnavaz

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18 SCI & Scopus indexed image processing journals

Image processing Scopus indexed journals : Scopus covers nearly 40000 research journals from various fields. Every quarter, Scopus maintains a list of journals (after indexing new journals and removing predatory journals from the list). This blog post aims to provide a list of Scopus indexed journals in image processing .

Also check: Fast publishing Computer science journals

Page Contents

List of Scopus indexed image processing journals

1. eurasip journal on image and video processing.

ISSN: 16875281 Publisher: Springer Nature Submit Manuscript

2. IET IMAGE PROCESSING

ISSN: Publisher: Wiley-Blackwell Submit Manuscript

3. IMAGE ANALYSIS & STEREOLOGY

ISSN: 18545165 Publisher: Slovenian Society for Stereology and Quantitative Image Analysis Submit Manuscript

4. IMAGE PROCESSING ON LINE

ISSN: 21051232 Publisher: IPOL – Image Processing on Line Submit Manuscript

5. JOURNAL OF REAL-TIME IMAGE PROCESSING

ISSN: 18618219 Publisher: Springer Nature Submit Manuscript

6. MEDICAL IMAGE ANALYSIS

ISSN: 13618423 Publisher: Elsevier Submit Manuscript

7. SIGNAL IMAGE AND VIDEO PROCESSING

ISSN: 1863-1703 Publisher: SPRINGER LONDON LTD Submit Manuscript

8. COMPUTER VISION AND IMAGE UNDERSTANDING

ISSN: 1090235X Publisher: Elsevier Submit Manuscript

9. IEEE TRANSACTIONS ON IMAGE PROCESSING

ISSN: Publisher: IEEE Submit Manuscript

10. IMAGE AND VISION COMPUTING

ISSN:  0262-8856 Publisher: Elsevier Submit Manuscript

11. INTERNATIONAL JOURNAL OF IMAGE AND DATA FUSION

ISSN: 19479824 Publisher: Taylor & Francis Submit Manuscript

12. INTERNATIONAL JOURNAL OF IMAGE AND GRAPHICS

ISSN: 17936756 Publisher: World Scientific Submit Manuscript

13. JOURNAL OF FLOW VISUALIZATION AND IMAGE PROCESSING

ISSN: 1940-4336 Publisher: Begell House Submit Manuscript

14. JOURNAL OF SIGNAL PROCESSING SYSTEMS FOR SIGNAL IMAGE AND VIDEO TECHNOLOGY

ISSN: 1939-8018 Publisher: SPRINGER Submit Manuscript

15. JOURNAL OF THE OPTICAL SOCIETY OF AMERICA A-OPTICS IMAGE SCIENCE AND VISION

ISSN: 1084-7529 Publisher: OPTICAL SOC AMER Submit Manuscript

16. JOURNAL OF VISUAL COMMUNICATION AND IMAGE REPRESENTATION

ISSN: Publisher: Elsevier Submit Manuscript

17. PATTERN RECOGNITION AND IMAGE ANALYSIS

ISSN: 15556212 Publisher: Pleiades Publishing Submit Manuscript

18. SIGNAL PROCESSING-IMAGE COMMUNICATION

ISSN: 0923-5965 Publisher: Elsevier Submit Manuscript

I hope that the Scopus indexed image processing journals list will help you.

Credit & Sources : Scopus and Clarivate

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JSmol Viewer

Research on defect diagnosis of transmission lines based on multi-strategy image processing and improved deep network.

1. Introduction

2. multi-strategy image processing, 2.1. image enhancement based on wavelet denoising, 2.2. multi-threshold segmentation based on hsv color space, 2.3. extraction of transmission line regions based on morphological processing, 3. detection of transmission line defects, 3.2. googlenet, 3.3. focal loss, 3.4. proposed method, 4. experimental results and analysis, 4.1. dataset introduction, 4.2. results and analysis, 5. conclusions, author contributions, data availability statement, conflicts of interest.

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Share and Cite

Gou, M.; Tang, H.; Song, L.; Chen, Z.; Yan, X.; Zeng, X.; Fu, W. Research on Defect Diagnosis of Transmission Lines Based on Multi-Strategy Image Processing and Improved Deep Network. Processes 2024 , 12 , 1832. https://doi.org/10.3390/pr12091832

Gou M, Tang H, Song L, Chen Z, Yan X, Zeng X, Fu W. Research on Defect Diagnosis of Transmission Lines Based on Multi-Strategy Image Processing and Improved Deep Network. Processes . 2024; 12(9):1832. https://doi.org/10.3390/pr12091832

Gou, Ming, Hao Tang, Lei Song, Zhong Chen, Xiaoming Yan, Xiangwen Zeng, and Wenlong Fu. 2024. "Research on Defect Diagnosis of Transmission Lines Based on Multi-Strategy Image Processing and Improved Deep Network" Processes 12, no. 9: 1832. https://doi.org/10.3390/pr12091832

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