Raw Camera Image Demosaicing using Finite Impulse Response Filtering on Commodity GPU Hardware using CUDA

In this paper, we investigate demosaicing of raw camera images on parallel architectures using CUDA. To generate high-quality results, we use the method of Malvar et al., which incorporates the gradient for edgesensing demosaicing. The method can be implemented as a collection of finite impulse response filters, which can easily be mapped to a parallel architecture. We investigated different trade-offs between memory operations and processor occupation to acquire maximum performance, and found a clear difference in optimization principles between different GPU architecture designs. We show that trade-offs are still important and not straightforward when using systems with massive fast processors and slower memory.