ePrints@IIScePrints@IISc Home | About | Browse | Latest Additions | Advanced Search | Contact | Help


Prabhakar, Ram K and Babu, Venkatesh R (2016) GHOSTING-FREE MULTI-EXPOSURE IMAGE FUSION IN GRADIENT DOMAIN. In: IEEE International Conference on Acoustics, Speech, and Signal Processing, MAR 20-25, 2016, Shanghai, PEOPLES R CHINA, pp. 1766-1770.

[img] PDF
IEEE_Int_Con_Aco_Spe_Sig_Pro_Pro_1766_2016.pdf - Published Version
Restricted to Registered users only

Download (948kB) | Request a copy
Official URL: http://dx.doi.org/10.1109/ICASSP.2016.7471980


This paper presents an algorithm to produce ghosting-free High Dynamic Range (HDR) image by fusing set of multiple exposed images in gradient domain. Recently proposed Gradient domain based exposure fusion method provides high quality result but the scope of which is limited to static camera without foreground object motion. The presence of moving objects/hand shake produces a set of misaligned images. The result of gradient domain approach on misaligned images suffers from ghosting artifacts. In order to produce better HDR image without image registration, we propose to create an aligned image set from input image set by photometric calibration. The gradient of aligned image set is then used to reconstruct the fused final image. The proposed algorithm tested on several publicly available dynamic image sets shows that resultant HDR image is ghosting-free and well exposed. Additionally, the proposed method is fast and thus can be used in consumer appliances such as mobile phones, portable devices with digital cameras.

Item Type: Conference Proceedings
Series.: International Conference on Acoustics Speech and Signal Processing ICASSP
Additional Information: Copy right for this article belongs to the IEEE, 345 E 47TH ST, NEW YORK, NY 10017 USA
Department/Centre: Division of Interdisciplinary Sciences > Supercomputer Education & Research Centre
Date Deposited: 20 Jan 2017 04:26
Last Modified: 05 Nov 2018 12:26
URI: http://eprints.iisc.ac.in/id/eprint/55932

Actions (login required)

View Item View Item