Full and partial encryption methods are important for subscription based content providers, such as internet and cable TV
pay channels. Providers need to be able to protect their products while at the same time being able to provide
demonstrations to attract new customers without giving away the full value of the content. If an algorithm were
introduced which could provide any level of full or partial encryption in a fast and cost effective manner, the applications
to real-time commercial implementation would be numerous. In this paper, we present a novel application of alpha
rooting, using it to achieve fast and straightforward scalable encryption with a single algorithm. We further present use
of the measure of enhancement, the Logarithmic AME, to select optimal parameters for the partial encryption. When
parameters are selected using the measure, the output image achieves a balance between protecting the important data in
the image while still containing a good overall representation of the image. We will show results for this encryption
method on a number of images, using histograms to evaluate the effectiveness of the encryption.
Enhancing an image in such a way that maintains image edges is a difficult problem. Many current methods for image
enhancement either smooth edges on a small scale while improving contrast on a global scale or enhance edges on a
large scale while amplifying noise on a small scale. One method which has been proposed for overcoming this is
anisotropic diffusion, which views each image pixel as an energy sync which interacts with the surrounding pixels based
upon the differences in pixel intensities and conductance values calculated from local edge estimates. In this paper, we
propose a novel image enhancement method which makes use of these smoothed images produced by diffusion methods.
The basic steps of this algorithm are: a) decompose an image into a smoothed image and a difference image, for
example by using anisotropic diffusion or as in Lee's Algorithm ; b) apply two image enhancement algorithms, such
as alpha rooting  or logarithmic transform shifting ; c) fuse these images together, for example by weighting the
two enhanced images and summing them for the final image. Computer simulations comparing the results of the
proposed method and current state-of-the-art enhancement methods will be presented. These simulations show the
higher performance, both on the basis of subjective evaluation and objective measures, of the proposed method over
This paper presents a method of image enhancement using an adaptive thresholding method based on the human visual system. We utilize a number of different enhancement algorithms applied selectively to the different regions of an image to achieve a better overall enhancement than applying a single technique globally. The presented method is useful for images that contain various regions of improper illumination. It is also practical for correcting shadows. This thresholding system allows various enhancement algorithms to be used on different sections of the image based on the local visual characteristics. It further allows the parameters to be tuned differently for the specific regions, giving a more visually pleasing output image.
We demonstrate the algorithm and present results for several high quality images as well as lower quality images such as those captured using a cell phone camera. We then compare and contrast our method to other state-of-the-art enhancement algorithms.
Image enhancement is the task of applying certain alterations to an input image such as to obtain a more visually
pleasing image. The alteration usually requires interpretation and feedback from a human evaluator of the output
resulting image. Therefore, image enhancement is considered a difficult task when attempting to automate the analysis
process and eliminate the human intervention. Furthermore, images that do not have uniform brightness pose a
challenging problem for image enhancement systems. Different kinds of histogram equalization techniques have been
employed for enhancing images that have overall improper illumination or are over/under exposed. However, these
techniques perform poorly for images that contain various regions of improper illumination or improper exposure.
In this paper, we introduce new human vision model based automatic image enhancement techniques, multi-histogram
equalization as well as local and adaptive algorithms. These enhancement algorithms address the previously mentioned
shortcomings. We present a comparison of our results against many current local and adaptive histogram equalization
methods. Computer simulations are presented showing that the proposed algorithms outperform the other algorithms in
two important areas. First, they have better performance, both in terms of subjective and objective evaluations, then
that currently used algorithms on a series of poorly illuminated images as well as images with uniform and non-uniform
illumination, and images with improper exposure. Second, they better adapt to local features in an image, in
comparison to histogram equalization methods which treat the images globally.
Image enhancement performance is currently judged subjectively, with no reliable manner of quantifying the results of an enhancement. Current quantitative measures rely on linear algorithms to determine contrast, leaving room for improvement. With the introduction of more complex enhancement algorithms, there is a need for an effective method of quantifying performance to select optimal parameters. In this paper, we present a logarithmic based image enhancement measure. We demonstrate its performance on real world images. The results will show the effectiveness of our measures to select optimal enhancement parameters for the enhancement algorithms.
Performance measures of image enhancement are traditionally subjective and have difficulty quantifying the improvement made by the algorithm. In this paper, we present the image enhancement measures and show how utilizing logarithmic arithmetic based addition, subtraction, and multiplication provides better results than previously used measures. In addition, for illustration of the performance of developed measures, we present a comprehensive study of several image enhancement algorithms from all three domains, including spatial, transform, and logarithmic algorithms.