Automated Correction and Optimized Contrast Enhancement of Multi-Line CCD Images

This dissertation addresses automated correction and optimized contrast enhancement of multi-line CCD images for inspection and surveillance applications, focusing on three topics: multi-line CCD imaging systems setup, automated correction of multi-line CCD images, and automatic optimized image contrast enhancement.

generation, low cost, etc. However, due to the physical separation of line CCD sensors for the red (R), green (G), blue (B) color channel, the color images acquired by multi-line CCD cameras intrinsically exhibit a color misalignment defect, which is expressed as that the edges of objects in the scene are separated by a certain number of pixels in the R, G, B color planes in the scan direction. This defect, if not corrected properly, can severely degrade the quality of multi-line CCD images and hence the applications of multi-line CCD cameras. We developed an algorithm to automatically correct the color misalignment problem in multi-line CCD images.

generation, low cost, etc. However, due to the physical separation of line CCD sensors for the red (R), green (G), blue (B) color channel, the color images acquired by multi-line CCD cameras intrinsically exhibit a color misalignment defect, which is expressed as that the edges of objects in the scene are separated by a certain number of pixels in the R, G, B color planes in the scan direction. This defect, if not corrected properly, can severely degrade the quality of multi-line CCD images and hence the applications of multi-line CCD cameras. We developed an algorithm to automatically correct the color misalignment problem in multi-line CCD images.

Contrast enhancement plays an important role in image processing applications. Conventional contrast enhancement techniques either often fail to produce satisfactory results for a broad variety of low-contrast images, or cannot be automatically applied to different images, because their processing parameters must be specified manually to produce a satisfactory result for a given image. However, the GLG technique doesn’t have the above drawbacks. The basic procedure of GLG is to first group the histogram components of a low-contrast image into a proper number of bins according to a new image contrast measure developed in this research, Average Pixel Distance on Grayscale (APDG); then remap these bins evenly over the grayscale, and finally ungroup the previously grouped gray-levels. Accordingly, this new technique is named gray-level grouping (GLG). GLG and its variations not only produce results superior to competing contrast enhancement techniques, but are also fully automatic in most circumstances, and are applicable to a broad variety of images.