Computationally scalable adaptive image interpolation algorithm using maximum-likelihood denoising for real-time applications

Abstract. Computationally scalable image interpolation algorithm is always desirable for software and hardware implementations on center processing unit (CPU), digital signal processor, field-programmable gate array, and low-cost hardware. A low-complexity, computationally scalable, and data-adaptive image interpolation algorithm that has a simple and homogeneous structure to efficiently scale the computation is proposed. Specifically, the image interpolation as a denoising problem is formulated by proposing a new image model to relate the observed low-resolution pixels and missing high-resolution pixels. Applying the maximum-likelihood estimation using the new image model results in an adaptive linear filter, where the filter coefficients depend on the local noise covariance matrix, which is estimated by local noise samples. Due to low overhead of the proposed interpolator, the overall computation efficiently scales with the number of noise samples. Experimental results show that the proposed scalable algorithm outperforms the state-of-the-art fast algorithms and achieves more than 36 frames per second for upscaling a 540 p (960×540) video to a 1080 p (1920×1080) video using multithreaded C++ software implementation on a PC system with Intel i7 950 3 GHz CPU.