Across-scale Process Similarity based Interpolation for Image Super-Resolution
This work addresses the problem of artifact-free image interpolation for super-resolution applications, but it is incremental as it builds on existing wavelet and optimization methods.
The paper tackled image super-resolution interpolation by exploiting process similarity between wavelet decompositions at different resolutions, using particle swarm optimization to generate high-resolution images. The proposed method achieved the fastest computation time and comparable results to existing techniques in terms of PSNR, SSIM, and FSIM measures.
A pivotal step in image super-resolution techniques is interpolation, which aims at generating high resolution images without introducing artifacts such as blurring and ringing. In this paper, we propose a technique that performs interpolation through an infusion of high frequency signal components computed by exploiting `process similarity'. By `process similarity', we refer to the resemblance between a decomposition of the image at a resolution to the decomposition of the image at another resolution. In our approach, the decompositions generating image details and approximations are obtained through the discrete wavelet (DWT) and stationary wavelet (SWT) transforms. The complementary nature of DWT and SWT is leveraged to get the structural relation between the input image and its low resolution approximation. The structural relation is represented by optimal model parameters obtained through particle swarm optimization (PSO). Owing to process similarity, these parameters are used to generate the high resolution output image from the input image. The proposed approach is compared with six existing techniques qualitatively and in terms of PSNR, SSIM, and FSIM measures, along with computation time (CPU time). It is found that our approach is the fastest in terms of CPU time and produces comparable results.