Motion deblurring based on fusing differently exposed images

Citations Over Time

Abstract

We present an approach to motion deblurring based on exploiting the information available in two differently exposed images of the same scene. Besides the normal-exposed image of the scene, we assume that a short exposed image is also available. Due to their different exposures the two images are degraded differently: the short exposed image is affected by noise, whereas the normal-exposed image could be affected by motion blur. The method presented in this paper estimates the motion blur point spread function (PSF) that models the degradation of the normal-exposed image, following to recover the image of the scene by deconvolution. The main processing steps detailed in the paper are: image registration and motion blur PSF estimation. Image registration operation includes a preprocessing step meant to cancel the differences between the two images due to their different exposures. Next, the registration parameters are estimated by matching the preprocessed images based on an image based registration approach. Motion blur PSF estimation is carried out by exploiting the difference between the degradation models of the two images, as well as certain prior assumptions about a typical motion blur PSF. Experiments and comparisons are presented in order to validate the proposed method.

Related Papers

• → Real-World Deep Local Motion Deblurring(2023)26 cited
• → Image Restoration Under Significant Additive Noise(2007)21 cited
• Survey on deblurring technology for motion images with sequence structure(2013)
• → Occlusion-Aware Video Deblurring with a New Layered Blur Model(2016)3 cited
• → Real-World Deep Local Motion Deblurring(2022)