Perceptually aware image inpainting

Abstract Image inpainting is a process of reconstructing missing regions, or removing unwanted objects automatically by propagating intensity and texture information from surrounding parts of the image in a visually plausible manner. We propose a new exemplar-based image inpainting algorithm, which uses the recently developed metric called the perceptual-fidelity aware mean squared error (PAMSE). The PAMSE is a Gaussian-smoothed mean squared error (MSE) and approximates a weighted sum of the gradient of MSE, the Laplacian of MSE, and MSE itself. We show that, compared to MSE, PAMSE is a promising perceptual fidelity metric for application to image inpainting and leads to better performance in propagating texture and geometric structure simultaneously.