Evidence map›Paper›PMID 34336160›Full record

ArticleJournal of healthcare engineering2021

VLSI Implementation of a High-Performance Nonlinear Image Scaling Algorithm.

Osamah Ibrahim Khalaf, Carlos Andrés Tavera Romero, A Azhagu Jaisudhan Pazhani, G Vinuja

Abstract read
In one paragraph

Article in Journal of healthcare engineering, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.

0numbers the graph read from it
0cells of the map it votes in
17citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

17 citing papers in PubMed.

  1. Article
  2. Machine Vision and Intelligent Algorithm Based on Neural Network.Computational intelligence and neuroscience · 2022
    Article
  3. Recognition of Human Body Feature Changes in Sports Health Based on Deep Learning.Computational and mathematical methods in medicine · 2022
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  12. Article
  13. Construction of Economic Data Management System Based on BP Neural Network.Computational intelligence and neuroscience · 2022
    Article
  14. Article
  15. Article
  16. Article
  17. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Osamah Ibrahim KhalafAl-Nahrain University, Baghdad, Iraq.ORCID 0000-0002-4750-8384
Carlos Andrés Tavera RomeroUniversidad Santiago de Cali, Cali, Colombia.
A Azhagu Jaisudhan PazhaniRamco Institute of Technology, Rajapalayam, Tamilnadu, India.ORCID 0000-0003-4339-2115
G VinujaVins Womens Christian College of Engineering, Kanyakumari, Tamilnadu, India.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This study implements the VLSI architecture for nonlinear-based picture scaling that is minimal in complexity and memory efficient. Image scaling is used to increase or decrease the size of an image in order to map the resolution of different devices, particularly cameras and printers. Larger memory and greater power are also necessary to produce high-resolution photographs. As a result, the goal of this project is to create a memory-efficient low-power image scaling methodology based on the effective weighted median interpolation methodology. Prefiltering is employed in linear interpolation scaling methods to improve the visual quality of the scaled image in noisy environments. By decreasing the blurring effect, the prefilter performs smoothing and sharpening processes to produce high-quality scaled images. Despite the fact that prefiltering requires more processing resources, the suggested solution scales via effective weighted median interpolation, which reduces noise intrinsically. As a result, a low-cost VLSI architecture can be created. The results of simulations reveal that the effective weighted median interpolation outperforms other existing approaches.

Indexed as

AlgorithmsImage Processing, Computer-AssistedHumans

Identifiers

PMID34336160
PMCPMC8321724

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.