Sparse Hartley Modeling for Fast Image Extrapolation

In many cases, image and video signal processing demands for high quality extrapolation algorithms, e.g., to solve inpainting problems or to increase image resolution. Indeed, a high computational load goes hand in hand with a good reconstruction quality as expensive models are calculated to estimate the missing data. To overcome this, the high-speed sparse Hartley modeling is introduced in this paper. This algorithm is based on Frequency Selective Extrapolation. In contrast to that, the model generation is carried out in the Hartley domain to exploit its real-valued transform properties. Due to this, it is possible to reduce the computational complexity significantly as no complex-valued arithmetic operations have to be conducted. In other words, a slightly higher reconstruction quality is obtained, while the proposed method is more than three times faster than the competing Frequency Selective Extrapolation.