Comparative evaluation of deblurring techniques for Fresnel lens computational imaging

Artem V. Nikonorov, Maksim V. Petrov, Sergey A. Bibikov, Yuriy V. Yuzifovich, Pavel Y. Yakimov, Nikolay Lvovich Kazanskiy, Roman V. Skidanov, Vladimir Fursov

Abstract: With suggested computational post-processing workflow for correcting optical distortions, the Fresnel lens can finally be used in lightweight and inexpensive computer vision sensors. Common methods for image enhancement do not comprehensively address the blurring artifacts caused by strong chromatic aberrations in images produced by a simple Fresnel optical system. To deliver image quality acceptable for general-purpose color imaging, we propose a computational post-capture processing to enhance the quality of images acquired with a 256-level Fresnel lens. The PSNR quality measure is then applied to estimate resulting quality for different deblurring techniques. A novel technique that removes chromatic blur without computationally expensive deconvolution can be considered a breakthrough as it finally enables in-camera embedded post-processing.