Recently image processing such as noise reduction, restoration, and super-resolution using the soft-partition-based weighted sum filters have shown state-of-the-art results. The partition-based weighted sum filters are spatially adaptive filtering techniques by combining vector quantization and linear finite impulse response filtering, which have been shown to achieve much better results than spatial-invariant filtering methods. However, they are computationally prohibitive for practical applications because of enormous computation involved in both filtering and training. Real-time filtering is impossible even for small image and window sizes. This paper presents fast implementations of the soft-partition-based weighted sum filtering by exploiting the massively parallel processing capabilities of a GPU within the CUDA framework. For the implementations, we focus on memory management and implementation strategies. The performance on various image and window sizes is measured and compared between the GPU-based and CPU-based implementations. The results show that the GPU-based implementations can significantly accelerate computations for the soft-partition-based weighted sum filtering, and make real-time image filtering possible.
In this paper we present an object boundary detection system using an off-the-shelf available 3D stereo monitor. Instead of implementing algorithms, the system’s image processing is based on utilizing the polarization feature of liquid-crystal display and the way the image is displayed on the 3D monitor to enhance object boundary. The users can view the enhanced object contour through a polarization glasses in real-time, which can be also recorded using a camera for further processing. A software is developed for user interaction and providing feedback to obtain the best detection results. The effectiveness of the proposed system is demonstrated using some medical and biological images. The proposed system has the advantages of real-time high speed processing, almost no numerical computation, and robustness to noise over the traditional methods using image processing algorithms.