الفهرس 
CHAPTER (1): IntroductionOnly 14 pages are availabe for public view
 |  | from | 105 |  |  |
| | | from | 105 | | |
Abstract
This thesis is concerned with the processing of infrared (IR) images. The
objective is to enhance the quality of these images as they have low contrast and low
SNR. Generally, the IR images are acquired with thermal cameras that can take still
images or video sequences. These cameras record the thermal distribution of the
object of interest and the background. Different sources of heat may lead to noise in
the obtained images and may lead also to low contrast around the object of interest. A
non-uniformity effect appears in the background of IR video frames with the heating
of the imaging camera or system. This effect appears as a fixed pattern noise in the
background. This fixed pattern noise leads to drift of the IR image histogram to the
right on the gray-scale axis. This problem is treated in this thesis with a Histogram
Matching (HM) treatment. The objective of this treatment is to correct the subsequent
frames in a video sequence based on the histogram of the first frame in the sequence
that does not suffer from the heating effect. That is why we adopt the HM as an
efficient tool for Non-Uniformity Correction (NUC) in IR video sequences.
In addition, this thesis presents an IR image enhancement scheme that
addresses the low-contrast problem depending on Contrast Limited Adaptive
Histogram Equalization (CLAHE) and fuzzy logic. The objective of this scheme is to
enhance the visual quality of IR images leading to better ability to discriminate the
object from the background. A new framework is proposed in this thesis for efficient
object detection from IR images. The IR image is first enhanced. After that, the image
gradient is obtained with a Laplacian operator. After that, the histogram of gradients is
estimated, and finally the cumulative histogram is obtained. The cumulative
histograms for images with and without objects are used for object detection. Certain
bins from these cumulative histograms are selected for detection based on estimated
thresholds. Simulation results show the success of the proposed techniques for NUC,
IR image enhancement and object detection from IR images.