Human Vision Inspired Based Image Illumination Enhancement by Using Local Singular Value Decomposition and Discrete Wavelet Transform

Gholamreza Anbarjafari; Cagri Ozcinar; Hasan Demirel

Recently, many computer vision applications are being inspired by human behavior, or human visual system. Also it is known that illumination issues have always been an important problem in many image processing applications. In this work we propose a new image illumination enhancement technique which is inspired from the human visual system behavior on illumination correction. The proposed technique uses local singular value decomposition (SVD) and discrete wavelet transforms (DWT), inspired from the fact that human visual system equalizes a scene by disregarding the extreme illuminated areas. In other words, human brain uses local illumination enhancement and this localization is based on the extreme illuminations, e.g. existence or absence of too much light. In this technique, after dividing the image into several locals, each local is converted into the DWT domain and after updating the singular value matrix of the respective low-low subband, the local is reconstructed by using inverse DWT (IDWT). Combination of locals results in the equalized image. The technique is compared with the standard general histogram equalization (GHE) and local histogram equalization (LHE). The experimental results are showing the superiority of the proposed method over the aforementioned techniques.

Select Volume / Issues:

• Volume 6 Issue 1, Jan. 2015

Year:

2015

Type of Publication:

Article

Keywords:

Illumination enhancement; Discrete wavelet transform; Singular value decomposition; Image processing

Journal:

IJECCE

Volume:

6

Number:

1

Pages:

14-19

Month:

Jan.-Feb.

ISSN:

2249-–0