Joint Burst Denoising and Demosaicking via Regularization and an Efficient Alignment | ||
| Journal of AI and Data Mining | ||
| دوره 8، شماره 4، بهمن 2020 اصل مقاله (20.17 M) | ||
| نوع مقاله: Applied Article | ||
| شناسه دیجیتال (DOI): 10.22044/jadm.2020.9193.2055 | ||
| نویسندگان | ||
| R. Azizi1؛ A. M. Latif* 2 | ||
| 1Department of Computer Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran | ||
| 2Computer Engineering Department, Engineering Faculty, Yazd University, Yazd, Iran | ||
| چکیده | ||
| In this work, we show that an image reconstruction from a burst of individually demosaicked RAW captures propagates demosaicking artifacts throughout the image processing pipeline. Hence, we propose a joint regularization scheme for burst denoising and demosaicking. We model the burst alignment functions and the color filter array sampling functions into one linear operator. Then, we formulate the individual burst reconstruction and the demosaicking problems into a three-color-channel optimization problem. We introduce a crosschannel prior to the solution of this optimization problem and develop a numerical solver via alternating direction method of multipliers. Moreover, our proposed method avoids the complexity of alignment estimation as a preprocessing step for burst reconstruction. It relies on a phase correlation approach in the Fourier’s domain to efficiently find the relative translation, rotation, and scale among the burst captures and to perform warping accordingly. As a result of these steps, the proposed joint burst denoising and demosaicking solution improves the quality of reconstructed images by a considerable margin compared to existing image model-based methods. | ||
| کلیدواژهها | ||
| Burst imaging؛ image demosaicking؛ Alternating Direction Method of Multipliers | ||
|
آمار تعداد مشاهده مقاله: 481 تعداد دریافت فایل اصل مقاله: 957 |
||