这里借助了IPOL上的一篇复现解读《An Analysis and Implementation of the HDR+ Burst Denoising Method》,来自GoPro的作者对论文进行了解读和复现,代码在 amonod/hdrplus-python: Open source Python implementation of the HDR+ photography pipeline (github.com)github.com/amonod/hdrplus-python 1.问题描述 ...
Google has proven that the burst photography method in HDR+ works well in the mobile photography context. Their implementation is robust enough to be used as the default camera setting, and resulting image quality has received rave reviews. While the technique has not yet been adopted by any ma...
For an interactive demo and the associated article, "An Analysis and Implementation of the HDR+ Burst Denoising Method", check out https://www.ipol.im/pub/art/2021/336/ Version numer and date: v1.0 (date of publication submission), 25/5/2021 Authors: Antoine Monod (antoine.monod@u-paris...
We call our method as Joint- HDRDN for that it performs HDR denoising and fusion jointly. The overall model is optimized by the Adam op- timizer [13] with default parameters. The batch size is set as 16, and the initial learning rate is 2e-4 and halved ...
Deep burst denoising. In Proc. of European Conference on Computer Vision, pages 538–554, 2018. 2 [12] Yulia Gryaditskaya, Tania Pouli, Erik Reinhard, Karol Myszkowski, and Hans-Peter Seidel. Motion aware exposure bracketing for HDR video. In Computer Graphics Forum, volume 34, ...
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Joint denoising and demosaicking with green channel prior for real-world burst images. IEEE Trans. Image Processing, 30:6930–6942, 2021. 4 [18] Jin Han, Chu Zhou, Peiqi Duan, Yehui Tang, Chang Xu, Chao Xu, Tiejun Huang, and Boxin Shi. Neuromorphic cam-...
In contrast, our method suppresses most of the noise and fuses the image to be ghosting-free. (a) (b) (c) (d) (e) Figure 7. Image fusion for denoising. (a) Input images. (b) Reference input. (c) Sen [5]. (d) Li12 [35]. (e) Ours. 4.2.4. Image Fusion for Traffic ...
In contrast, our method suppresses most of the noise and fuses the image to be ghosting-free. Figure 7. Image fusion for denoising. (a) Input images. (b) Reference input. (c) Sen [5]. (d) Li12 [35]. (e) Ours. 4.2.4. Image Fusion for Traffic Scenes We apply our fine-tuned...
In contrast, our method suppresses most of the noise and fuses the image to be ghosting-free. Figure 7. Image fusion for denoising. (a) Input images. (b) Reference input. (c) Sen [5]. (d) Li12 [35]. (e) Ours. 4.2.4. Image Fusion for Traffic Scenes We apply our fine-tuned...