The restoration of vibration-blurred images using measured motion function is considered. Since the blurring filter is of the finite impulse response type, the inverse one is of the all-pole infinite impulse response type. Direct application of the inverse filter to restore images blurred by vibration is attractive because of reduced computation requirements. Space domain filtering allows high parallelization of the process and reduction of required processor speed. However, a pure inverse filter provides excessive noise amplification and is possibly unstable. The proposed technique is to construct a modified inverse filter with preserved all-pole structure and optimized noise and stability properties. A mathematical concept was developed using z-transform properties. An experiment testing the proposed technique was setup and the results indicate restoration is 60%–80% of that possible with ideal Wiener filtering. However, the reduced computation and high parallelization can facilitate real-time restoration.
The restoration of vibration-blurred images using measured motion function is considered. Since the blurring filter is of finite impulse response type, the inverse one is of all-pole infinite impulse response type. Direct application of the inverse filter to restore images blurred by vibration is attractive because of minimal computation requirements. However, a pure inverse filter provides excessive noise amplification and is possibly unstable. The proposed technique is to construct a modified inverse filter with preserved all-pole structure and optimized noise and stability properties. An experiment testing the proposed technique was set up and the results presented here.
KEYWORDS: Digital signal processing, Video, Image filtering, Analog electronics, Electronic filtering, Image processing, Signal to noise ratio, Analog filtering, Filtering (signal processing), Cameras
We describe novel architecture for a real-time image restoration system of live TV signals. No DSP is involved. The spatial filtering is obtained from two electronic analog filters, one for the raster lines and one for the columns. The very fast response of analog filters is the key for truly real-time video frame rate performance. The digital part of the system serves the purpose of pipe-lined parallel data conversion and flow, but not that of image processing at all. Despite the lack of DSP, this architecture exhibits some very important advantages. It does not need any computational source, it is very fast, and it is much cheaper. Also our 'parallel analog computer' can be easily incorporated in any complex system with video signal data as a simple 'plug-in' between the camera and monitor. An important aspect is that the system carries lower digitalization noise than DSP, thus yielding better SNR characteristics at a lower price. The system is not bound to nay specific kind of spatial frequency filtering and can be electronically tuned to obtain exact performance parameters. Because of these advantages, this architecture is promising for a wide variety of system such as supermarket multicamera security, military and aerospace vision systems, and medical diagnostics.
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