INTEGRATED LOW LIGHT IMAGE ENHANCEMENT IN TRANSPORTATION SYSTEM

Recent Intelligent Transportation System (ITS) focuses on both traffic management and Homeland Security. It involves advance detection systems of all kind but proper analysis of the image data is required for controlling and further processing. It becomes even more difficult when it comes to low light images due to limitation in the image sensor and heavy amount of noise. An ITS supports all levels like (Transport policy level, Traffic control tactical level, Traffic control measure level, Traffic control operation). For this it uses several split systems like Real time passenger information (RTPI), Automatic Number Plate Recognition (ANPR), Variable message signs (VMS), Vehicle to Infrastructure (V2I) and Vehicle to Vehicle (V2V) system. While analyzing critical scenarios, mostly for the development of the application for Vehicle to Infrastructure (V2I) System several cases are taken into consideration. From these cases some are very difficult to analyze due to the visibility of the background as the detail structure is taken into consideration. Here Direct processing of low light images or video frames like day images leads to loss of required data, so an efficient enhancement method is required which gives allowable result for further transformation and analysis with minimal processing. So an Adaptive Enhancement Method is presented here which applies different enhancement methods for day light and low light images separately. For this purpose a combination of image fusion, edge detection filtering and Contourlet transformation is used for low light images; tone level adjustment and low level feature extraction for enhancement of day light images

3D TARGET DETECTION USING STACKED HOLOGRAM

A very popular technique of 3D vision now-a-days is holography which has many advantages over the stereoscopic 3D vision. The same technique can be implemented on RADAR to take high resolution 3D picture of the target and to track with very minute displacement. As this does not employ parallax method, so binocular antenna can be replaced by a single antenna. Again in this thesis another new concept, gated range, is implemented, i.e., the target can be detected within a certain range on spatial domain so that it can focus to the target and the clutter has no effect on it. Narrow virtual transmit pulses are synthesized by differencing long-duration, staggered pulse repetition interval (PRI) transmit pulses. PRI is staggered at an intermediate frequency IF. Echoes from virtual pulses form IF-modulated interference patterns with a reference wave. Samples of interference patterns are IF-filtered to produce high spatial resolution holographic data. PRI stagger can be very small, e.g., 1-ns, to produce a 1-ns virtual pulse from very long, staggered transmit pulses. Occupied Bandwidth (OBW) can be less than 10 MHz due to long RF pulses needed for holography, while spatial resolution can be very high, corresponding to ul tra-wideband (UWB) operation, due to short virtual pulses. X-Y antenna scanning can produce range-gated surface holograms from quadrature data. Multiple range gates can produce stacked-in-range holograms. Motion and vibration can be detected by changes in interference patterns within a range-gated zone

Adaptive Motion Detection for Image Deblurring in RTS Controller

Abstract: An Adaptive method for Image Deblurring is presented here. Processing of image data collected from both surveillance camera and on road traffic control motor vehicle camera is a big issue because often the objects are in motion and sometimes both the objects and camera are not steady. This leads to Blurring of the image and further image processing is not possible due to the degradation of received image. So Image Deblurring techniques are applied before enhancement or further processing. But it needs proper data for Deblurring like the frequency characteristics (Point Spread Function (PSF)) and Noise characteristics (Noise-to-Signal Power Ratio(NSR)). The method presented here gives the above information along with the motion information. The information about motion detection is very important because in the Deblurring process the noise estimation cannot be done without knowing actual pixels of the sensor noise present in the image. So to get a deblurred image with proper noise reduction that can be further processed in the RTS (Road Traffic & Safety) controller required information are provided sequentially according to the motion detection and Deblurring algorithm. This method uses some good Deblurring methods like Blind Deconvolution and Regularization filtering along with proper motion detections and characteristics estimations to get an image close to the true image which is sufficient for further processing