Date of Award

Fall 2007

Document Type

Thesis

Degree Name

Master of Science (MS)

Department

Electrical & Computer Engineering

Program/Concentration

Electrical Engineering

Committee Director

Vijayan K. Asari

Committee Member

Zia-ur Rahman

Committee Member

Jiang Li

Call Number for Print

Special Collections LD4331.E55 A75 2007

Abstract

In night time surveillance, there is a possibility of having extremely bright and dark regions in some image frames of a video sequence. Neither the object details in the low intensity areas nor in the high intensity areas can be clearly interpreted. Several image processing techniques have been developed to retrieve meaningful information under low lighting conditions. The algorithm based on integrated neighborhood dependency of pixel characteristics, and that based on the illuminance reflectance model perform well for improving the visual quality of digital images captured under extremely low and nonuniform lighting conditions. But these techniques cannot perform well in over-illuminated regions in an image. Multi-windowed inverse sigmoid is capable of simultaneously enhancing the brighter and darker regions in an image. But the control parameters in this algorithm have to be computed based on the images under consideration.

In this thesis, a new nonlinear image enhancement algorithm, based on an image dependent non linear function, Locally Tuned Sine Nonlinearity (LTSN), is proposed for enhancing extremely high contrast images. The control parameter in this algorithm is determined adaptively based on image statistics. The proposed algorithm is capable of compressing bright regions and at the same time enhancing dark regions by preserving the main structure of the illuminance - reflectance characteristics. The main core of the algorithm is a new nonlinear sine transfer function that is very flexible in enhancing the dark regions and compressing overexposed regions in an image. A neighborhood dependent approach is employed for contrast enhancement. The Laplacian filtered image (reflectance) is added to the enhanced image to preserve the fine details. The quality of the enhanced image is further improved by applying a contrast stretch process. A basic linear color restoration process based on the chromatic information of the original image is employed to convert the enhanced intensity image back to a color image. It is observed that the proposed algorithm yields visually optimal results on images captured under extreme lighting conditions. It is envisaged that the new technique would be useful for improving the visibility of scenes of night time driving and night security situations.

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DOI

10.25777/wpn9-wh08

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