GPU based acceleration architecture for image enhancement in spatial domain

In order to reduce the processing time of image enhancement in spatial domain, a GPU (Graphic Processing Unit) based acceleration architecture is proposed and implemented. With structured design method, computing model, data and algorithm resource which are indispensability in GPU computation are encapsulated, and computed directly in high performance with CUDA (Compute Unified Device Architecture). This architecture shields the configuration details of GPU computation and reduces repetitive work. In addition, new algorithms of enhancement in spatial domain could be added conveniently in the architecture. More importantly, the executing time of algorithms could be reduced 12-38 times than before in CPU, which is useful for the applications in real time system. For the neighborhood algorithms of image enhancement, a better solution of texture memory is used. Though this way, the time of executing algorithms could be reduced 36-135 times.