We have designed a terahertz imaging system, built with electronic components and operating at a single tunable
frequency. The system scans in hybrid mode, combining coarse mechanical positioning with a fine scan produced
by perturbing the beam with a system of opaque masks, placed into the collimated beam. The mask set is
based on a modified Hadamard design, which aims at minimizing the loss of power and noise effects. The image
acquisition is performed in transmission mode, with the sample placed at the focal plane. We present several
imaging results obtained using our technique.
In spite the prodigious growth in the market for digital cameras, they have yet to displace film-based cameras in the consumer market. This is largely due to the high cost of photographic resolution sensors. One possible approach to producing a low cost, high resolution sensor is to linearly scan a masked low resolution sensor. Masking of the sensor elements allows transform domain imaging. Multiple displaced exposures of such a masked sensor permits the device to acquire a linear transform of a higher resolution representation of the image than that defined by the sensor element dimensions. Various approaches have been developed in the past along these lines, but they often suffer from poor sensitivity, difficulty in being adapted to a 2D sensor or spatially variable noise response. This paper presents an approach based on a new class of Hadamard masks--Uniform Noise Hadamard Masks--which has superior sensitivity to simple sampling approaches and retains the multiresolution capabilities of certain Hadamard matrices, while overcoming the non-uniform noise response problems of some simple Hadamard based masks.
KEYWORDS: Fractal analysis, Image compression, Data centers, Data modeling, Data compression, Signal to noise ratio, Computer programming, Image processing, Computer simulations, Electrical engineering
This paper presents a fast block matching technique for image data compression based on fractal models. In fractal coding, domain blocks in an image are searched and the one most similar to a range block is selected as the best matching domain block. We propose a fast search method to improve the encoding time and the data compression rate. In our method a positive aim at a domain block consists of the inner pixels of the range block and the outer only one or two pixels of the range block. The method has been tested on real image data with good results.
A fractal-based method for color image compression is
presented. The method transforms the direct color components into three approximate principal components and applies a fractal-based compression method developed for gray-scale images to each new
component. The main principal component, which contains a large amount of energy, is coded with high accuracy, while the other two components can be coded at lower accuracy and a very high compression ratio. The principal-component-based method gives an overall higher quality in the reconstructed image at a similar
compression rate compared with compression based on other linear transforms of the color space.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.