In surveillance applications, the visibility of details within an image is necessary to ensure
detection. However, bright spots in images can occupy most of the dynamic range of the
sensor, causing lower energy details to appear dark and difficult to see. In addition, shadows
from structures such as buildings or bridges obscure features within the image, further limiting
contrast. Dynamic range compression and contrast enhancement algorithms can be used to
improve the visibility of these low energy details. In this paper, we propose a locally adaptive
contrast enhancement algorithm based on the multi-scale wavelet transform to compress the
dynamic range of images as well as increase the visibility of details obscured by shadows.
Using an edge detector as the mother wavelet, this algorithm operates by increasing the gain of
low energy gradient magnitudes provided by the wavelet transform, while simultaneously
decreasing the gain of higher energy gradient magnitudes. Limits on the amount of gain
imposed are set locally to prevent the over-enhancement of noise. The results of using the
proposed method on aerial images show that this method outperforms common methods in its
ability to enhance small details while simultaneously preventing ringing artifacts and noise
over-enhancement.
Vast amounts of video footage are being continuously acquired by surveillance systems on private premises, commercial
properties, government compounds, and military installations. Facial recognition systems have the potential to identify
suspicious individuals on law enforcement watchlists, but accuracy is severely hampered by the low resolution of typical
surveillance footage and the far distance of suspects from the cameras. To improve accuracy, super-resolution can
enhance suspect details by utilizing a sequence of low resolution frames from the surveillance footage to reconstruct a
higher resolution image for input into the facial recognition system. This work measures the improvement of face
recognition with super-resolution in a realistic surveillance scenario. Low resolution and super-resolved query sets are
generated using a video database at different eye-to-eye distances corresponding to different distances of subjects from
the camera. Performance of a face recognition algorithm using the super-resolved and baseline query sets was calculated
by matching against galleries consisting of frontal mug shots. The results show that super-resolution improves
performance significantly at the examined mid and close ranges.
Given the frequent lack of a reference image or ground truth when performance testing Bayer pattern color filter array
(CFA) demosaicing algorithms, two new no-reference quality assessment algorithms are proposed. These new quality
assessment algorithms give a relative comparison of two demosaicing algorithms by measuring the presence of two
common artifacts in their output images. For this purpose, various demosaicing algorithms are reviewed, especially
adaptive color plane, gradient based methods, and median filtering, with particular attention paid to the false color and
edge blurring artifacts common to all demosaicing algorithms. Classic quality assessment methods which require a
reference image, such as MSE, PSNR, and ΔE, are reviewed, their typical usage characterized, and their associated
pitfalls identified. With this information in mind, the motivations for no-reference quality assessment are discussed. The
new quality assessment algorithms are then designed for a relative comparison of two images demosaiced from the same
CFA data by measuring the sharpness of the edges and determining the presence of false colors. Demosaicing algorithms
described earlier are evaluated and ranked using these new algorithms. A large quantity of real images is given for
review. These images are also used to justify those rankings suggested by the new quality assessment algorithms. This
work provides a path forward for future research investigating possible relationships between CFA demosaicing and
color image super-resolution.
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