This paper reviews the AIM 2019 challenge on constrained example-based single image super-resolution with focus on proposed solutions and results. The challenge had 3 tracks. Taking the three main aspects (i.e., number of parameters, inference/running time, fidelity (PSNR)) of MSR-ResNet as the baseline, Track 1 aims to reduce the amount of parameters while being constrained to maintain or improve the running time and the PSNR result, Tracks 2 and 3 aim to optimize running time and PSNR result with constrain of the other two aspects, respectively. Each track had an average of 64 registered participants, and 12 teams submitted the final results. They gauge the state-of-the-art in single image super-resolution.
A depth-dependent dispersion compensation algorithm for enhancing the image quality of the Fourier-domain optical coherence tomography (OCT) is presented. The dispersion related with depth in the sample is considered. Using the iterative method, an analytical formula for compensating the depth-dependent dispersion in the sample is obtained. We apply depth-dependent dispersion compensation algorithm to process the phantom images and in vivo images. Using sharpness metric based on variation coefficient to compare the results processed with different dispersion compensation algorithms, we find that the depth-dependent dispersion compensation algorithm can improve image quality at full depth.
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