Skip to main content
SpringerLink
Log in

Using pivot image for the development of composite visual space based on image normalization

  • Focus
  • Published:
Soft Computing Aims and scope Submit manuscript

Abstract

Digital videos have numerous applications, ranging from amateur videos on social media to complex imaging of space objects. Most of the time, different images and videos are combined to get a large field of view, including a simple panoramic image or detailed images of Mars created by stitching over 900 images. In most cases, different images are stitched linearly on a plane. However, recently researchers have stitched images and videos together to produce 360-degree views. In these 360-degree videos, there is primarily the main camera or a set of cameras that covers the scene. In contrast, another concept—the 3D view—captures depth along with the height and width of the images. The proposed work focuses on developing a composite visual space that captures a scene with different cameras and combines it accordingly. One of the essential features of the proposed work is image normalization. Images acquired from multiple sources are of various sizes, orientations, and brightness levels. A set of eight augmented normalized images are formed in a circular form where each image has its positional features. The article focuses on the normalization process of the images captured from different cameras with different specifications so that they can be used to form the proposed visual space. The results of the proposed algorithm are compared for time and space. The proposed algorithm uses 45–70% less computation. On average, this method normalizes with only 52% of computations for the selected dataset. This proposed algorithm us less computational and storage resource. In term if computational, it is faster as most of the calculations involve shifts in the integer values and the range of the values are from 0 to 255 that can fit in 8-bit integer. Most of the other methods uses complex real number equations that are computationally expansive and use more bits per pixels. Moreover, this approach requires a smaller number of shifts because of which quality is affected almost insignificantly.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19

Similar content being viewed by others

Data availability

The data are available upon request from the corresponding author.

References

  • Asokan A, Popescu DE, Anitha J, Hemanth DJ (2020) Bat algorithm based non-linear contrast stretching for satellite image enhancement,". Geosciences 10:78

    Article  Google Scholar 

  • Azevedo RGDA, Birkbeck N, De Simone F, Janatra I, Adsumilli B, Frossard P (2019) Visual distortions in 360-degree videos. arXiv preprint http://arxiv.org/abs/1901.01848

  • Balali V, Depwe E, Golparvar-Fard M (2015) Multi-class traffic sign detection and classification using google street view images. In: Transportation research board 94th annual meeting, transportation research board, Washington

  • Corbillon X, Simon G, Devlic A, Chakareski J (2017a) Viewport-adaptive navigable 360-degree video delivery. In: 2017a IEEE international conference on communications (ICC), pp 1–7

  • Corbillon X, De Simone F, Simon G (2017b) 360-degree video head movement dataset. In: Proceedings of the 8th ACM on multimedia systems conference, pp 199–204

  • Cyganek B, Siebert JP (2011) An introduction to 3D computer vision techniques and algorithms. Wiley

    MATH  Google Scholar 

  • Dhal KG, Quraishi MI, Das S (2017) An improved cuckoo search based optimal ranged brightness preserved histogram equalization and contrast stretching method. Int J Swarm Intell Res (IJSIR) 8:1–29

    Article  Google Scholar 

  • Gorai A, Ghosh A (2009) Gray-level image enhancement by particle swarm optimization. In: 2009 world congress on nature and biologically inspired computing (NaBIC), pp 72–77

  • Heo J, FitzHugh TW (2000) A standardized radiometric normalization method for change detection using remotely sensed imagery. Photogramm Eng Rem Sens 66:173–181

    Google Scholar 

  • Iqbal K, Salam RA, Osman A, Talib AZ (2007) Underwater Image enhancement using an integrated colour model. IAENG Int J Comput Sci 34:2

  • Iizuka K (2006) Welcome to the wonderful world of 3D: introduction, principles and history. Opt Photon News 17:42–51

    Article  Google Scholar 

  • Jagatheeswari P, Kumar SS, Rajaram M (2009) Contrast stretching recursively separated histogram equalization for brightness preservation and contrast enhancement. In: 2009 international conference on advances in computing, control, and telecommunication technologies, pp 111–115

  • Kotkar VA, Gharde SS (2013) Review of various image contrast enhancement techniques. Int J Innov Res Sci Eng Technol. https://doi.org/10.17577/IJERTV2IS110302

  • Janani P, Premaladha J, Ravichandran K (2015) Image enhancement techniques: a study. Indian J Sci Technol 8:1–12

    Article  Google Scholar 

  • Jones A, McDowall I, Yamada H, Bolas M, Debevec P (2007) Rendering for an interactive 360 light field display. In: ACM SIGGRAPH 2007 papers, pp 40

  • Kim J-H, Kim J-H, Jung S-W, Ko S-J, Noh C-K (2011) Novel contrast enhancement scheme for infrared image using detail-preserving stretching. Opt Eng 50:077002

    Article  Google Scholar 

  • Kumar T, Perumal S, Krishnan N (2011) Fuzzy based contrast stretching for medical image enhancement. Comput Spec Issue Fuzzy 6956:233–236

    Google Scholar 

  • Lee K, Moorthy AK, Lee S, Bovik AC (2013) 3D visual activity assessment based on natural scene statistics. IEEE Trans Image Process 23:450–465

    MathSciNet  MATH  Google Scholar 

  • Li C, Zhang W, Liu Y, Wang Y (2019) Very long term field of view prediction for 360-degree video streamin. In: 2019 IEEE conference on multimedia information processing and retrieval (MIPR), pp 297–302

  • Liu Q, Chen M, Zhou D (2015) Single image haze removal via depth-based contrast stretching transform. Sci China Inf Sci 58:1–17

    Article  Google Scholar 

  • Makandar A, Patrot A, Halalli B (2014) Color image analysis and contrast stretching using histogram equalization. Int J Adv Inf Sci Technol (IJAIST) 27:119–125

    Google Scholar 

  • Marques O (2011) Digital video processing techniques and applications

  • Mukhopadhyay S, Chanda B (2000) A multiscale morphological approach to local contrast enhancement. Signal Process 80:685–696

    Article  MATH  Google Scholar 

  • Munteanu C, Lazarescu V (1999) Evolutionary contrast stretching and detail enhancement of satellite images. In: Proc. Mendel, pp 94–99

  • Mustapha A, Oulefki A, Bengherabi M, Boutellaa E, Algaet MA (2017) Towards nonuniform illumination face enhancement via adaptive contrast stretching. Multimed Tools Appl 76:21961–21999

    Article  Google Scholar 

  • Nagelli P, Reddy CL, Reddy BN (2014) Blurred image enhancement using contrast stretching, local edge detection and blind deconvolution. Int J Inf Comput Technol 4:0974–2239

    Google Scholar 

  • Nazir F, Riaz M, Ghafoor A, Arif F (2015) Brief communication: contrast-stretching-and histogram-smoothness-based synthetic aperture radar image enhancement for flood map generation. Nat Hazard 15:273–276

    Article  Google Scholar 

  • Negi SS, Bhandari YS (2014) A hybrid approach to image enhancement using contrast stretching on image sharpening and the analysis of various cases arising using histogram. In: International conference on recent advances and innovations in engineering (ICRAIE-2014), pp 1–6

  • Nguyen A, Yan Z, Nahrstedt K (2018) Your attention is unique: detecting 360-degree video saliency in head-mounted display for head movement prediction. In: Proceedings of the 26th ACM international conference on multimedia, pp 1190–1198

  • Osman M, Mashor M, Saad Z, Jaafar H (2009) Contrast enhancement for Ziehl-Neelsen tissue slide images using linear stretching and histogram equalization technique. In: 2009 IEEE symposium on industrial electronics and applications, pp 431–435

  • Papadopoulos A, Fotiadis DI, Costaridou L (2008) Improvement of microcalcification cluster detection in mammography utilizing image enhancement techniques. Comput Biol Med 38:1045–1055

    Article  Google Scholar 

  • Park E, Yang J, Yumer E, Ceylan D, Berg AC (2017) Transformation-grounded image generation network for novel 3d view synthesis. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 3500–3509

  • Prasanna RD, Neelamegam P, Sriram S, Raju N (2012) Enhancement of vein patterns in hand image for biometric and biomedical application using various image enhancement techniques. Proc Eng 38:1174–1185

    Article  Google Scholar 

  • Rajput S, Suralkar S (2013) Comparative study of image enhancement techniques. Int J Comput Sci Mob Comput 2:11–21

    Google Scholar 

  • Ravindraiah R (2012) Quality improvement for analysis of leukemia images through contrast stretch methods. Proc Eng 30:475–481

    Article  Google Scholar 

  • Rogowska J, Preston K, Sashin D (1988) Evaluation of digital unsharp masking and local contrast stretching as applied to chest radiographs. IEEE Trans Biomed Eng 35:817–827

    Article  Google Scholar 

  • Saad IA, George LE (2014) Robust and fast iris localization using contrast stretching and leading edge detection. Int J Emerg Trends Technol Comput Sci (IJETTCS) 3:61–67

    Google Scholar 

  • Singh TR, Singh KM (2010) Image enhancement by adaptive power-law transformations. Bahria Univ J Inf Commun Technol (BUJICT) 3(1)

  • Srinivas C, KS NP, Zakariah M, Alothaibi YA, Shaukat K, Partibane B et al (2022) Deep transfer learning approaches in performance analysis of brain tumor classification using MRI images. J Healthc Eng 2022:3264367. https://doi.org/10.1155/2022/3264367

  • Srinivasan S, Balram N (2006) Adaptive contrast enhancement using local region stretching. In: Proceedings of the 9th Asian symposium on information display, pp 152–155

  • Sun X, Shi L, Luo Y, Yang W, Li H, Liang P et al (2015) Histogram-based normalization technique on human brain magnetic resonance images from different acquisitions. Biomed Eng Online 14:1–17

    Article  Google Scholar 

  • Toh LB, Mashor M, Ehkan P, Rosline H, Junoh A, Harun NH (2016) Implementation of high dynamic range rendering on acute leukemia slide images using contrast stretching. In: 2016 3rd international conference on electronic design (ICED), pp 491–496

  • Trongtirakul T, Chiracharit W, Imberman S, Agaian S (2020) Fractional contrast stretching for image enhancement of aerial and satellite images. Electron Imaging 63(6):60411–1

    Google Scholar 

  • Vettehen PH, Wiltink D, Huiskamp M, Schaap G, Ketelaar P (2019) Taking the full view: how viewers respond to 360-degree video news. Comput Hum Behav 91:24–32

    Article  Google Scholar 

  • Vishwakarma AK, Mishra A (2012) Color image enhancement techniques: a critical review. Indian J Comput Sci Eng 3:39–45

    Google Scholar 

  • Wang B, Song W, Tian Y, Lu Y, Li Y, Guo J (2023) Applying plasma acoustic and image information for underwater LIBS normalization. J Anal At Spectrom 38:281–292

  • Xu B, Zhuang Y, Tang H, Zhang L (2010) Object-based multilevel contrast stretching method for image enhancement. IEEE Trans Consum Electron 56:1746–1754

    Article  Google Scholar 

  • Yelmanov S, Romanyshyn Y (2018) Image enhancement in automatic mode by piecewise nonlinear contrast stretching. In: 2018 IEEE first international conference on system analysis and intelligent computing (SAIC), pp 1–6

  • Zou D, Yang B (2023) Infrared and low-light visible image fusion based on hybrid multiscale decomposition and adaptive light adjustment. Opt Lasers Eng 160:107268

    Article  Google Scholar 

Download references

Funding

No funding was received to support the study.

Author information

Authors and Affiliations

  1. Institute of Management Sciences, Hayatabad, Peshawar, 25100, Pakistan

    Fouzia Idrees & Awais Adnan

  2. Shaheed Benazir Bhutto Women University, Laramah, Peshawar, 25000, Pakistan

    Fouzia Idrees

Authors
  1. Fouzia Idrees
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Awais Adnan
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Fouzia Idrees.

Ethics declarations

Conflict of interest

The authors declare no conflict of interest.

Ethical approval

The paper does not deal with any ethical problems.

Informed consent

We declare that all the authors have informed consent.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Idrees, F., Adnan, A. Using pivot image for the development of composite visual space based on image normalization. Soft Comput (2023). https://doi.org/10.1007/s00500-023-08075-2

Download citation

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s00500-023-08075-2

Keywords

Search

Navigation