New Paper: “Accelerating Translational Image Registration for HDR Images on GPU”

Hello academy-lovers!

Our new study is now available on arXiv:

K.C. Alpay, K.B. Aydemir, A. Temizel, Accelerating Translational Image Registration for HDR Images on GPU, arXiv:2007.06483, July 2020.

https://arxiv.org/abs/2007.06483

Our paper got accepted for publication in High Performance Computing Conference: Basarim 2020.

Click here to watch our conference presentation.

paper image
LDR alignment prevents blurry artifacts in the final HDR image.

Abstract: High Dynamic Range (HDR) images are generated using multiple exposures of a scene. When a hand-held camera is used to capture a static scene, these images need to be aligned by globally shifting each image in both dimensions. For a fast and robust alignment, the shift amount is commonly calculated using Median Threshold Bitmaps (MTB) and creating an image pyramid. In this study, we optimize these computations using a parallel processing approach utilizing GPU. Experimental evaluation shows that the proposed implementation achieves a speed-up of up to 6.24 times over the baseline multi-threaded CPU implementation on the alignment of one image pair. The source code is available at https://github.com/kadircenk/WardMTBCuda

Hope to see you in the next studies!

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