Additive and Multiplicative Noise Removal Framework for Large Scale Color Satellite Images on OpenMP and GPUs

The satellite images are usually contaminated with multiplicative noises and some additive noises [1, 2]. Due to the large size of images, the removal process of these two types of noises at real-time is time consuming. The use of many-core processors such as GPUs may be advantageous in reducing the time of denoising. However, with the limitation of the GPU memory and the memory transfer cost, the proper design for denoising the large images is required. In this paper, we introduce the novel method for denoising both additive and multiplicative noises on multiple GPUs. The method is extended from [8] to perform a large-image denoising. It considers the proper data fitting to the GPU memory, memory utilization and thread utilization on both the CPU and GPUs. The speedup on the computation time of upto 87.29 times can be achieved compared with the sequential computation on the color 4096×4096 satellite image.