Exhaustive Analysis of Image Enhancement Using Point-to-Point Transformation

  • Pratima ManhasEmail author
  • Shaveta Thakral
  • Parveen Arora
Conference paper
Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 65)


Image can be processed via variety of transformation techniques. Transformations of signals not only extract useful information from any real signal but also process it as per demanding application. Image transformation can be histogram equalization, discrete wavelet transform, morphology or point transformation. Point transformation just improves visual appearance of an image without fetching any new information. It is just required for detection or extraction of some information as per demanding application. Point transformation can be point-to-point transformation, local-to-point transformation, or global-to-point transformation. Transformation technique can be chosen based on complexity of transformation technique. The aim of this paper is to discuss point-to-point transformation technique which is advantageous in terms of constant complexity compared to local to point and global-to-point techniques as it is calculated per pixel and it is point to point. Different types of image enhancement techniques such as image negative, separation of image into RGB parts, gamma correction and log transformation are implemented using MATLAB.


Pixel Point transformation Image Signal Local Global Enhancement 


  1. 1.
    Beucher S, Meyer F (1992) The morphological approach to segmentation: the watershed transformation. In: Optical engineering, vol 34. Marcel Dekker Incorporated, New York, pp 433–433Google Scholar
  2. 2.
    Chang Y, Saito S, Nakajima M (2007) Example-based color transformation of image and video using basic color categories. IEEE Trans Image Process 16(2):329–336MathSciNetCrossRefGoogle Scholar
  3. 3.
    Manhas P, Thakral S (2018) Comparative analysis of different wavelet filters image enhancement using histogram equalization. In: International conference on materials, applied physics and engineering (ICMAE)Google Scholar
  4. 4.
    Lee MC, Chen WG (1999) Microsoft Corp.: image compression and affine transformation for image motion compensation. U.S. Patent 5,970,173Google Scholar
  5. 5.
    Prucnal PR, Saleh BE (1981) Transformation of image-signal-dependent noise into image-signal-independent noise. Opt Lett 6(7):316–318CrossRefGoogle Scholar
  6. 6.
    Zhang, X, Wandell BA (1997) A spatial extension of CIELAB for digital color‐image reproduction. J Soc Inf Disp 5(1):61–63CrossRefGoogle Scholar
  7. 7.
    Umeyama S (1991) Least-squares estimation of transformation parameters between two point patterns. IEEE Trans Pattern Anal Mach Intell 13(4):376–380 CrossRefGoogle Scholar
  8. 8.
    Qi D, Zou J, Han X (2000) A new class of scrambling transformation and its application in the image information covering. Sci China Ser E Technol Sci 43(3):304–312MathSciNetCrossRefGoogle Scholar
  9. 9.
    Lin H, Du P, Zhao W, Zhang L, Sun H (2010) Image registration based on corner detection and affine transformation. In: 2010 3rd international congress on image and signal processing (CISP), vol 5. IEEE, pp 2184–2188Google Scholar
  10. 10.
    Beier T, Neely S (1992) Feature-based image metamorphosis. In: ACM SIGGRAPH computer graphics, vol 26, no 2. ACM, pp 35–42Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Pratima Manhas
    • 1
    Email author
  • Shaveta Thakral
    • 1
  • Parveen Arora
    • 2
  1. 1.ECE DepartmentFET, Manav Rachna International Institute of Research & StudiesFaridabadIndia
  2. 2.NEC Technologies India Pvt LtdBengaluruIndia

Personalised recommendations