Example of IET Image Processing format
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Example of IET Image Processing format Example of IET Image Processing format Example of IET Image Processing format Example of IET Image Processing format Example of IET Image Processing format Example of IET Image Processing format Example of IET Image Processing format Example of IET Image Processing format Example of IET Image Processing format
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Example of IET Image Processing format Example of IET Image Processing format Example of IET Image Processing format Example of IET Image Processing format Example of IET Image Processing format Example of IET Image Processing format Example of IET Image Processing format Example of IET Image Processing format Example of IET Image Processing format
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open access Open Access

IET Image Processing — Template for authors

Publisher: IET Publications
Categories Rank Trend in last 3 yrs
Electrical and Electronic Engineering #289 of 693 down down by 27 ranks
Signal Processing #51 of 108 up up by 3 ranks
Computer Vision and Pattern Recognition #42 of 85 down down by 5 ranks
Software #204 of 389 down down by 4 ranks
journal-quality-icon Journal quality:
Good
calendar-icon Last 4 years overview: 1221 Published Papers | 3966 Citations
indexed-in-icon Indexed in: Scopus
last-updated-icon Last updated: 22/07/2020
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Related Journals

open access Open Access

Elsevier

Quality:  
High
CiteRatio: 6.3
SJR: 0.544
SNIP: 1.494
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Elsevier

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SJR: 0.907
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IEEE

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CiteRatio: 11.4
SJR: 1.005
SNIP: 2.547
open access Open Access
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Elsevier

Quality:  
High
CiteRatio: 15.7
SJR: 1.492
SNIP: 3.419

Journal Performance & Insights

Impact Factor

CiteRatio

Determines the importance of a journal by taking a measure of frequency with which the average article in a journal has been cited in a particular year.

A measure of average citations received per peer-reviewed paper published in the journal.

1.995

0% from 2018

Impact factor for IET Image Processing from 2016 - 2019
Year Value
2019 1.995
2018 2.004
2017 1.401
2016 1.044
graph view Graph view
table view Table view

3.2

3% from 2019

CiteRatio for IET Image Processing from 2016 - 2020
Year Value
2020 3.2
2019 3.1
2018 2.8
2017 2.4
2016 2.3
graph view Graph view
table view Table view

insights Insights

  • Impact factor of this journal has decreased by 0% in last year.
  • This journal’s impact factor is in the top 10 percentile category.

insights Insights

  • CiteRatio of this journal has increased by 3% in last years.
  • This journal’s CiteRatio is in the top 10 percentile category.

SCImago Journal Rank (SJR)

Source Normalized Impact per Paper (SNIP)

Measures weighted citations received by the journal. Citation weighting depends on the categories and prestige of the citing journal.

Measures actual citations received relative to citations expected for the journal's category.

0.401

9% from 2019

SJR for IET Image Processing from 2016 - 2020
Year Value
2020 0.401
2019 0.442
2018 0.348
2017 0.34
2016 0.307
graph view Graph view
table view Table view

1.167

11% from 2019

SNIP for IET Image Processing from 2016 - 2020
Year Value
2020 1.167
2019 1.315
2018 1.255
2017 1.224
2016 1.017
graph view Graph view
table view Table view

insights Insights

  • SJR of this journal has decreased by 9% in last years.
  • This journal’s SJR is in the top 10 percentile category.

insights Insights

  • SNIP of this journal has decreased by 11% in last years.
  • This journal’s SNIP is in the top 10 percentile category.

IET Image Processing

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IET Publications

IET Image Processing

The IET Image Processing journal encompasses research areas related to the generation, processing and communication of visual information. The focus of the journal is the coverage of the latest research results in image and video processing, including image generation and disp...... Read More

Engineering

i
Last updated on
22 Jul 2020
i
ISSN
1751-9659
i
Impact Factor
Medium - 0.945
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Acceptance Rate
Not provided
i
Frequency
Not provided
i
Open Access
Yes
i
Sherpa RoMEO Archiving Policy
Green faq
i
Plagiarism Check
Available via Turnitin
i
Endnote Style
Download Available
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Citation Type
Numbered
[25]
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Bibliography Example
Blonder, G.E., Tinkham, M., Klapwijk, T.M.: ‘Transition from metallic to tunneling regimes in superconducting microconstrictions: Excess current, charge imbalance, and supercurrent conversion’, Phys Rev B, 1982, 25, (7), pp. 4515–4532. Available from: 10.1103/PhysRevB.25.4515

Top papers written in this journal

Journal Article DOI: 10.1049/IET-IPR.2012.0455
Retinal vessel segmentation by improved matched filtering: evaluation on a new high-resolution fundus image database
22 Jul 2013 - Iet Image Processing

Abstract:

Automatic assessment of retinal vessels plays an important role in the diagnosis of various eye, as well as systemic diseases. A public screening is highly desirable for prompt and effective treatment, since such diseases need to be diagnosed at an early stage. Automated and accurate segmentation of the retinal blood vessel t... Automatic assessment of retinal vessels plays an important role in the diagnosis of various eye, as well as systemic diseases. A public screening is highly desirable for prompt and effective treatment, since such diseases need to be diagnosed at an early stage. Automated and accurate segmentation of the retinal blood vessel tree is one of the challenging tasks in the computer-aided analysis of fundus images today. We improve the concept of matched filtering, and propose a novel and accurate method for segmenting retinal vessels. Our goal is to be able to segment blood vessels with varying vessel diameters in high-resolution colour fundus images. All recent authors compare their vessel segmentation results to each other using only low-resolution retinal image databases. Consequently, we provide a new publicly available high-resolution fundus image database of healthy and pathological retinas. Our performance evaluation shows that the proposed blood vessel segmentation approach is at least comparable with recent state-of-the-art methods. It outperforms most of them with an accuracy of 95% evaluated on the new database. read more read less

Topics:

Fundus (eye) (57%)57% related to the paper, Image segmentation (56%)56% related to the paper
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371 Citations
Journal Article DOI: 10.1049/IET-IPR.2014.0311
Simultaneous image fusion and denoising with adaptive sparse representation
Yu Liu1, Zengfu Wang1
30 Apr 2015 - Iet Image Processing

Abstract:

In this study, a novel adaptive sparse representation (ASR) model is presented for simultaneous image fusion and denoising. As a powerful signal modelling technique, sparse representation (SR) has been successfully employed in many image processing applications such as denoising and fusion. In traditional SR-based application... In this study, a novel adaptive sparse representation (ASR) model is presented for simultaneous image fusion and denoising. As a powerful signal modelling technique, sparse representation (SR) has been successfully employed in many image processing applications such as denoising and fusion. In traditional SR-based applications, a highly redundant dictionary is always needed to satisfy signal reconstruction requirement since the structures vary significantly across different image patches. However, it may result in potential visual artefacts as well as high computational cost. In the proposed ASR model, instead of learning a single redundant dictionary, a set of more compact sub-dictionaries are learned from numerous high-quality image patches which have been pre-classified into several corresponding categories based on their gradient information. At the fusion and denoising processes, one of the sub-dictionaries is adaptively selected for a given set of source image patches. Experimental results on multi-focus and multi-modal image sets demonstrate that the ASR-based fusion method can outperform the conventional SR-based method in terms of both visual quality and objective assessment. read more read less

Topics:

Image fusion (61%)61% related to the paper, Sparse approximation (59%)59% related to the paper, Image processing (59%)59% related to the paper, Contextual image classification (53%)53% related to the paper, Signal reconstruction (52%)52% related to the paper
View PDF
284 Citations
Journal Article DOI: 10.1049/IET-IPR.2015.0385
Classification of malignant melanoma and benign skin lesions: implementation of automatic ABCD rule
Reda Kasmi1, Karim Mokrani1
01 Jun 2016 - Iet Image Processing

Abstract:

The ABCD (asymmetry, border irregularity, colour and dermoscopic structure) rule of dermoscopy is a scoring method used by dermatologists to quantify dermoscopy findings and effectively separate melanoma from benign lesions. Automatic detection of the ABCD features and separation of benign lesions from melanoma could enable e... The ABCD (asymmetry, border irregularity, colour and dermoscopic structure) rule of dermoscopy is a scoring method used by dermatologists to quantify dermoscopy findings and effectively separate melanoma from benign lesions. Automatic detection of the ABCD features and separation of benign lesions from melanoma could enable earlier detection of melanoma. In this study, automatic ABCD scoring of dermoscopy lesions is implemented. Pre-processing enables automatic detection of hair using Gabor filters and lesion boundaries using geodesic active contours. Algorithms are implemented to extract the characteristics of ABCD attributes. Methods used here combine existing methods with novel methods to detect colour asymmetry and dermoscopic structures. To classify lesions as melanoma or benign nevus, the total dermoscopy score is calculated. The experimental results, using 200 dermoscopic images, where 80 are malignant melanomas and 120 benign lesions, show that the algorithm achieves 91.25% sensitivity of 91.25 and 95.83% specificity. This is comparable to the 92.8% sensitivity and 90.3% specificity reported for human implementation of the ABCD rule. The experimental results show that the extracted features can be used to build a promising classifier for melanoma detection. read more read less
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187 Citations
Journal Article DOI: 10.1049/IET-IPR.2015.0150
Combination of contrast limited adaptive histogram equalisation and discrete wavelet transform for image enhancement
Huang Lidong1, Zhao Wei1, Wang Jun1, Sun Zebin1
17 Sep 2015 - Iet Image Processing

Abstract:

Image enhancement has an important role in image processing applications. Contrast limited adaptive histogram equalisation (CLAHE) is an effective algorithm to enhance the local details of an image. However, it faces the contrast overstretching and noise enhancement problems. To solve these problems, this study presents a nov... Image enhancement has an important role in image processing applications. Contrast limited adaptive histogram equalisation (CLAHE) is an effective algorithm to enhance the local details of an image. However, it faces the contrast overstretching and noise enhancement problems. To solve these problems, this study presents a novel image enhancement method, named CLAHE-discrete wavelet transform (DWT), which combines the CLAHE with DWT. The new method includes three main steps: First, the original image is decomposed into low-frequency and high-frequency components by DWT. Then, the authors enhance the low-frequency coefficients using CLAHE and keep the high-frequency coefficients unchanged to limit noise enhancement. This is because the high-frequency component corresponds to the detail information and contains most noises of original image. Finally, reconstruct the image by taking inverse DWT of the new coefficients. To alleviate over-enhancement, the reconstructed and original images are averaged using an originally proposed weighting factor. The weighting operation can control the enhancement levels of regions with different luminances in original image adaptively. This is important because bright parts of image are usually needless to be enhanced in comparison with the dark parts. Extensive experiments show that this method performs well in detail preservation and noise suppression. read more read less

Topics:

Image restoration (65%)65% related to the paper, Top-hat transform (62%)62% related to the paper, Adaptive histogram equalization (61%)61% related to the paper, Image processing (60%)60% related to the paper, Image noise (58%)58% related to the paper
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173 Citations
Journal Article DOI: 10.1049/IET-IPR.2014.0965
Block-based discrete wavelet transform-singular value decomposition image watermarking scheme using human visual system characteristics
Nasrin M. Makbol1, Bee Ee Khoo1, Taha H. Rassem2
01 Jan 2016 - Iet Image Processing

Abstract:

Digital watermarking has been suggested as a way to achieve digital protection. The aim of digital watermarking is to insert the secret data into the image without significantly affecting the visual quality. This study presents a robust block-based image watermarking scheme based on the singular value decomposition (SVD) and ... Digital watermarking has been suggested as a way to achieve digital protection. The aim of digital watermarking is to insert the secret data into the image without significantly affecting the visual quality. This study presents a robust block-based image watermarking scheme based on the singular value decomposition (SVD) and human visual system in the discrete wavelet transform (DWT) domain. The proposed method is considered to be a block-based scheme that utilises the entropy and edge entropy as HVS characteristics for the selection of significant blocks to embed the watermark, which is a binary watermark logo. The blocks of the lowest entropy values and edge entropy values are selected as the best regions to insert the watermark. After the first level of DWT decomposition, the SVD is performed on the low-low sub-band to modify several elements in its U matrix according to predefined conditions. The experimental results of the proposed scheme showed high imperceptibility and high robustness against all image processing attacks and several geometrical attacks using examples of standard and real images. Furthermore, the proposed scheme outperformed several previous schemes in terms of imperceptibility and robustness. The security issue is improved by encrypting a portion of the important information using Advanced Standard Encryption a key size of 192-bits (AES-192). read more read less

Topics:

Watermark (63%)63% related to the paper, Digital watermarking (58%)58% related to the paper, Discrete wavelet transform (55%)55% related to the paper, Human visual system model (54%)54% related to the paper, Image processing (53%)53% related to the paper
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160 Citations
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Frequently asked questions

1. Can I write IET Image Processing in LaTeX?

Absolutely not! Our tool has been designed to help you focus on writing. You can write your entire paper as per the IET Image Processing guidelines and auto format it.

2. Do you follow the IET Image Processing guidelines?

Yes, the template is compliant with the IET Image Processing guidelines. Our experts at SciSpace ensure that. If there are any changes to the journal's guidelines, we'll change our algorithm accordingly.

3. Can I cite my article in multiple styles in IET Image Processing?

Of course! We support all the top citation styles, such as APA style, MLA style, Vancouver style, Harvard style, and Chicago style. For example, when you write your paper and hit autoformat, our system will automatically update your article as per the IET Image Processing citation style.

4. Can I use the IET Image Processing templates for free?

Sign up for our free trial, and you'll be able to use all our features for seven days. You'll see how helpful they are and how inexpensive they are compared to other options, Especially for IET Image Processing.

5. Can I use a manuscript in IET Image Processing that I have written in MS Word?

Yes. You can choose the right template, copy-paste the contents from the word document, and click on auto-format. Once you're done, you'll have a publish-ready paper IET Image Processing that you can download at the end.

6. How long does it usually take you to format my papers in IET Image Processing?

It only takes a matter of seconds to edit your manuscript. Besides that, our intuitive editor saves you from writing and formatting it in IET Image Processing.

7. Where can I find the template for the IET Image Processing?

It is possible to find the Word template for any journal on Google. However, why use a template when you can write your entire manuscript on SciSpace , auto format it as per IET Image Processing's guidelines and download the same in Word, PDF and LaTeX formats? Give us a try!.

8. Can I reformat my paper to fit the IET Image Processing's guidelines?

Of course! You can do this using our intuitive editor. It's very easy. If you need help, our support team is always ready to assist you.

9. IET Image Processing an online tool or is there a desktop version?

SciSpace's IET Image Processing is currently available as an online tool. We're developing a desktop version, too. You can request (or upvote) any features that you think would be helpful for you and other researchers in the "feature request" section of your account once you've signed up with us.

10. I cannot find my template in your gallery. Can you create it for me like IET Image Processing?

Sure. You can request any template and we'll have it setup within a few days. You can find the request box in Journal Gallery on the right side bar under the heading, "Couldn't find the format you were looking for like IET Image Processing?”

11. What is the output that I would get after using IET Image Processing?

After writing your paper autoformatting in IET Image Processing, you can download it in multiple formats, viz., PDF, Docx, and LaTeX.

12. Is IET Image Processing's impact factor high enough that I should try publishing my article there?

To be honest, the answer is no. The impact factor is one of the many elements that determine the quality of a journal. Few of these factors include review board, rejection rates, frequency of inclusion in indexes, and Eigenfactor. You need to assess all these factors before you make your final call.

13. What is Sherpa RoMEO Archiving Policy for IET Image Processing?

SHERPA/RoMEO Database

We extracted this data from Sherpa Romeo to help researchers understand the access level of this journal in accordance with the Sherpa Romeo Archiving Policy for IET Image Processing. The table below indicates the level of access a journal has as per Sherpa Romeo's archiving policy.

RoMEO Colour Archiving policy
Green Can archive pre-print and post-print or publisher's version/PDF
Blue Can archive post-print (ie final draft post-refereeing) or publisher's version/PDF
Yellow Can archive pre-print (ie pre-refereeing)
White Archiving not formally supported
FYI:
  1. Pre-prints as being the version of the paper before peer review and
  2. Post-prints as being the version of the paper after peer-review, with revisions having been made.

14. What are the most common citation types In IET Image Processing?

The 5 most common citation types in order of usage for IET Image Processing are:.

S. No. Citation Style Type
1. Author Year
2. Numbered
3. Numbered (Superscripted)
4. Author Year (Cited Pages)
5. Footnote

15. How do I submit my article to the IET Image Processing?

It is possible to find the Word template for any journal on Google. However, why use a template when you can write your entire manuscript on SciSpace , auto format it as per IET Image Processing's guidelines and download the same in Word, PDF and LaTeX formats? Give us a try!.

16. Can I download IET Image Processing in Endnote format?

Yes, SciSpace provides this functionality. After signing up, you would need to import your existing references from Word or Bib file to SciSpace. Then SciSpace would allow you to download your references in IET Image Processing Endnote style according to Elsevier guidelines.

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I spent hours with MS word for reformatting. It was frustrating - plain and simple. With SciSpace, I can draft my manuscripts and once it is finished I can just submit. In case, I have to submit to another journal it is really just a button click instead of an afternoon of reformatting.

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