Parallel Computing Accelerated Image Inpainting using GPU CUDA, Theano, and Tensorflow

Image inpainting refers to image restoration process that reconstruct damaged image to obtain it lost information based on existing information. PDE-based approach is commonly used for image interpolation especially inpainting. Since PDE process express convolution and continuous change, the approach may take a lot of computational resources and will run slow on standard computer CPU. To overcome that, GPU parallel computing method for PDE-based image inpainting are proposed. These days, some handy platform or frameworks to utilize GPU are already exist like CUDA, Theano, and Tensorflow. CUDA is well-known as parallel computing platform and programming model to work with programming language such as C/C++. In other hand Theano and Tensorflow is a bit different thing, both of them is a machine learning framework based on Python that also able to utilize GPU. Although Theano and Tensorflow are specialized for machine learning and deep learning, the system is general enough to applied for computational process like image inpainting. The results of this work show benchmark performance of PDE image inpainting running on CPU using C+ +, Theano, and Tensorflow and on GPU with CUDA, Theano, and Tensorflow. The benchmark shows that parallel computing accelerated PDE image inpainting can run faster on GPU either with CUDA, Theano, or Tensorflow compared to PDE image inpainting running on CPU.