This paper discusses the use of constraints when super-resolving passive millimeter wave (PMMW) images. A PMMW imager has good all-weather imaging capability but requires a large collection aperture to obtain adequate spatial resolution due to the diffraction limit and the long wavelengths involved. A typical aperture size for a system operating at 94GHz would be 1m in diameter. This size may be reduced if image restoration techniques are employed. A factor of two in recognition range may be achieved using a linear technique such as a Wiener filter; while a factor of four is available using non-linear techniques. These non-linear restoration methods generate the missing high frequency information above the pass band in band limited images. For this bandwidth extension to generate genuine high frequencies, it is necessary to restore the image subject to constraints. These constraints should be applied directly to the scene content rather than to any noise that might also be present. The merits of the available super-resolution techniques are discussed with particular reference to the Lorentzian method. Attempts are made to explain why the distribution of gradients within an image is Lorentzian by assuming that an image has randomly distributed gradients of random size. Any increase in sharpness of an image frequently results in an increase in the noise present. The effect of noise and image sharpness on the ability of a human observer to recognise an object in the scene is discussed with reference to a recent model of human perception.
This paper reviews the mathematical image processing methods available for super-resolving passive millimeter wave (PMMW) images. PMMW imaging has a number of advantages over infra- red (IR) and visible imaging in being able to operate under adverse weather conditions making it useful for all weather surveillance. The main disadvantage, however, is the size of aperture required to obtain usable spatial resolution. A typical aperture size would be 1 m diameter for a system operating at 94GHz. This aperture may be reduced if super- resolution techniques are employed. To achieve super- resolution non-linear methods of restoration are required in order to generate missing high frequency information. For thee to be genuine high frequencies it is necessary to restore the image subject to constraints. These constraints should apply directly to the scene content rather than to properties of any noise also present. The merits of the available super-resolution techniques are discussed with reference to sharpening noisy PMMW images. Any increase in sharpness of an image frequently results in an increase in the noise present. This can detract from the ability of a human observer to recognize an object in the scene. This problem is discussed with reference to a recent model of human perception.
A non-linear image restoration method has been developed by combining the advantages of the existing Lorentzian and Wiener filter techniques. It sharpens edges without introducing Gibbs ringing and restores the background without flattening it. An image is separated into features and background regions, the features are restored using the Lorentzian method and the background is sharpened using a Wiener filter. The Wiener filter is applied to the second derivative of the background to avoid ringing introduced by discontinuities where feature shave been removed. Also three pre-processing techniques are described that suppress fixed pattern noise, temporal noise and scan-lines from video data. The fixed pattern noise is suppressed by subtracting one frame of a moving image from another. Then the difference image is deconvoled with a function based on the translation of the image between each frame. Temporal noise is suppressed by calculating the displacement between frames and averaging the frames in their displaced position. Scan- lines are suppress by blurring the image in a direction perpendicular to the scan-lines and fitting the original image to the blurred image by adjusting gains and offsets. Examples of each method are provided.
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.