Morphological Background Detection and Enhancement of Images With Poor Lighting

In this paper, some morphological transformations are used to detect the background in images characterized by poor lighting. Lately, contrast image enhancement has been carried out by the application of two operators based on the Weber's law notion. The first operator employs information from block analysis, while the second transformation utilizes the opening by reconstruction, which is employed to define the multibackground notion. The objective of contrast operators consists in normalizing the grey level of the input image with the purpose of avoiding abrupt changes in intensity among the different regions. Finally, the performance of the proposed operators is illustrated through the processing of images with different backgrounds, the majority of them with poor lighting conditions.

[1]  H. Heijmans Morphological image operators , 1994 .

[2]  J. Serra,et al.  Contrasts and activity lattice , 1989 .

[3]  David J. Kriegman,et al.  From few to many: generative models for recognition under variable pose and illumination , 2000, Proceedings Fourth IEEE International Conference on Automatic Face and Gesture Recognition (Cat. No. PR00580).

[4]  Petros Maragos,et al.  Morphological filters-Part I: Their set-theoretic analysis and relations to linear shift-invariant filters , 1987, IEEE Trans. Acoust. Speech Signal Process..

[5]  Gilberto Herrera Ruiz,et al.  Contrast Enhancement and Illumination Changes Compensation , 2007 .

[6]  Ernst Heinrich Weber,et al.  De pulsu, resorptione, auditu et tactu. Annotationes anatomicae et physiologicae , 1834 .

[7]  E. Peli Contrast in complex images. , 1990, Journal of the Optical Society of America. A, Optics and image science.

[8]  Pierre Soille,et al.  Morphological Image Analysis: Principles and Applications , 2003 .

[9]  Sandy Irani,et al.  Perception-based contrast enhancement of images , 2007, TAP.

[10]  I. Terol-Villalobos Morphological image enhancement and segmentation , 2001 .

[11]  Philippe Salembier,et al.  Connected operators and pyramids , 1993, Optics & Photonics.

[12]  I. Terol-Villalobos Morphological connected contrast mappings based on top-hat criteria: A multiscale contrast approach , 2004 .

[13]  Philippe Salembier,et al.  Flat zones filtering, connected operators, and filters by reconstruction , 1995, IEEE Trans. Image Process..

[14]  Josef Kittler,et al.  A comparison of photometric normalisation algorithms for face verification , 2004, Sixth IEEE International Conference on Automatic Face and Gesture Recognition, 2004. Proceedings..

[15]  G. Johnson,et al.  Regionally Adaptive Histogram Equalization of the Chest , 1987, IEEE Transactions on Medical Imaging.

[16]  Zicheng Liu,et al.  Learning-Based Perceptual Image Quality Improvement for Video Conferencing , 2007, 2007 IEEE International Conference on Multimedia and Expo.

[17]  Bhabatosh Chanda,et al.  A multiscale morphological approach to local contrast enhancement , 2000, Signal Process..

[18]  Luc Vincent,et al.  Morphological grayscale reconstruction in image analysis: applications and efficient algorithms , 1993, IEEE Trans. Image Process..

[19]  Josef Kittler,et al.  Photometric normalisation for component-based face verification , 2006, 7th International Conference on Automatic Face and Gesture Recognition (FGR06).

[20]  Alexander Toet,et al.  Multiscale contrast enhancement with applications to image fusion , 1992 .

[21]  Jerzy Kasperek Real Time Morphological Image Contrast Enhancement in Virtex FPGA , 2001, FPL.

[22]  Edward Dougherty,et al.  Morphological Segmentation for Textures and Particles , 2020 .