Fast Two-step Blind Optical Aberration Correction

TL;DR

This paper introduces a fast, two-step blind optical aberration correction method that estimates local blur kernels and removes chromatic aberrations using neural networks, improving image sharpness without prior camera info.

Contribution

The paper presents a novel two-stage approach combining kernel estimation and neural network-based chromatic correction for blind optical aberration correction.

Findings

Achieves state-of-the-art correction performance.

Operates efficiently on raw and JPEG images.

Competes with commercial non-blind algorithms.

Abstract

The optics of any camera degrades the sharpness of photographs, which is a key visual quality criterion. This degradation is characterized by the point-spread function (PSF), which depends on the wavelengths of light and is variable across the imaging field. In this paper, we propose a two-step scheme to correct optical aberrations in a single raw or JPEG image, i.e., without any prior information on the camera or lens. First, we estimate local Gaussian blur kernels for overlapping patches and sharpen them with a non-blind deblurring technique. Based on the measurements of the PSFs of dozens of lenses, these blur kernels are modeled as RGB Gaussians defined by seven parameters. Second, we remove the remaining lateral chromatic aberrations (not contemplated in the first step) with a convolutional neural network, trained to minimize the red/green and blue/green residual images. Experiments on both synthetic and real images show that the combination of these two stages yields a fast state-of-the-art blind optical aberration compensation technique that competes with commercial non-blind algorithms.