Tootfinder

An approximate history of my amateur phone photography:
1. Really poor photos. Unsavory interest in (low resolution) panoramas.
2. Photos get a little better. Sometimes they're HDRs (I suspect the camera app was set to "auto").
3. A new phone. Higher resolution and a gyroscope.
4. Switching to LineageOS, and therefore to #OpenCamera. HDR enabled unconditionally.
5. HDR does not always come great (compared to the stock Motorola app that doesn't work anymore). I enable saving component photos, so I could try getting a better quality combination using the PC. I never manage that.
6. I start experimenting with exposure correction (combined with HDR). Sometimes I do multiple photos with different "Eves" to choose the best one.
7. A new phone. Finally, given even the gyroscope was failing already.
8. I learn that Pixels have "HDR " that gets activated when you do standard mode photos. For a while I do both standard and HDR photos; also I compare the middle component image with the standard mode image. [https://opencamera.sourceforge.io/help.html#faq]
9. Before I reach any final conclusions, I read "What is HDR, anyway?" I disable HDR entirely, instead I save standard mode (i.e. HDR ) raw images (which presumably aren't affected by HDR ) [https://www.lux.camera/what-is-hdr/]. Not that I ever managed to get anything good out of raws.
10. I discover that I can switch the lens. Today I've made my first photos, switching lens to get optical zoom 💪.

Getting there with SpacemiT K1x image building in @… :
running 2 tests
test starfive::visionfive2_hdr::test_hdr ... ok
test spacemit::k1x_hdr::sign ... ok
test result: ok. 2 passed; 0 failed; 0 ignored; 0 measured; 0 filtered out; finished in 0.00s
Alright, time for some fresh air!

Computational Imaging for Enhanced Computer Vision
Humera Shaikh, Kaur Jashanpreet
https://arxiv.org/abs/2509.08712 https://arxiv.org/pdf/2509.08712…

Computational Imaging for Enhanced Computer Vision
This paper presents a comprehensive survey of computational imaging (CI) techniques and their transformative impact on computer vision (CV) applications. Conventional imaging methods often fail to deliver high-fidelity visual data in challenging conditions, such as low light, motion blur, or high dynamic range scenes, thereby limiting the performance of state-of-the-art CV systems. Computational imaging techniques, including light field imaging, high dynamic range (HDR) imaging, deblurring, hig…

Deep Visual Odometry for Stereo Event Cameras
Sheng Zhong, Junkai Niu, Yi Zhou
https://arxiv.org/abs/2509.08235 https://arxiv.org/pdf/2509.08235

Deep Visual Odometry for Stereo Event Cameras
Event-based cameras are bio-inspired sensors with pixels that independently and asynchronously respond to brightness changes at microsecond resolution, offering the potential to handle state estimation tasks involving motion blur and high dynamic range (HDR) illumination conditions. However, the versatility of event-based visual odometry (VO) relying on handcrafted data association (either direct or indirect methods) is still unreliable, especially in field robot applications under low-light HD…

Another sunrise shot from Myrtle Beach this morning. This one took a bit of massaging to get it to match what I intended for it. Raw image processed into bracketed-exposure tif files, run through Luminence HDR to create a composite HDR image, then composited again with the original exposure and selective parts of the HDR rendering erased to let the original show through (mostly around the sun and highlights on the water). Purposely left a little uncanny valley, "This is an HDR Image&quo…

Dolby launches Dolby Vision 2, adding AI-powered Content Intelligence and Authentic Motion, first on Hisense TVs, over 10 years after launching Dolby Vision (Emma Roth/The Verge)
https://www.theverge.com/news/768842/dolby-vision-2-launch-content-i…

Dolby Vision 2 goes beyond HDR with more AI and ‘authentic motion’ smoothing
Dolby has announced Dolby Vision 2, which offers new “Content Intelligence” to optimize your TV based on what you’re watching and where you’re watching it.

SeHDR: Single-Exposure HDR Novel View Synthesis via 3D Gaussian Bracketing
Yiyu Li, Haoyuan Wang, Ke Xu, Gerhard Petrus Hancke, Rynson W. H. Lau
https://arxiv.org/abs/2509.20400

SeHDR: Single-Exposure HDR Novel View Synthesis via 3D Gaussian Bracketing
This paper presents SeHDR, a novel high dynamic range 3D Gaussian Splatting (HDR-3DGS) approach for generating HDR novel views given multi-view LDR images. Unlike existing methods that typically require the multi-view LDR input images to be captured from different exposures, which are tedious to capture and more likely to suffer from errors (e.g., object motion blurs and calibration/alignment inaccuracies), our approach learns the HDR scene representation from multi-view LDR images of a single …

EfficienT-HDR: An Efficient Transformer-Based Framework via Multi-Exposure Fusion for HDR Reconstruction
Yu-Shen Huang, Tzu-Han Chen, Cheng-Yen Hsiao, Shaou-Gang Miaou
https://arxiv.org/abs/2509.19779 …

EfficienT-HDR: An Efficient Transformer-Based Framework via Multi-Exposure Fusion for HDR Reconstruction
Achieving high-quality High Dynamic Range (HDR) imaging on resource-constrained edge devices is a critical challenge in computer vision, as its performance directly impacts downstream tasks such as intelligent surveillance and autonomous driving. Multi-Exposure Fusion (MEF) is a mainstream technique to achieve this goal; however, existing methods generally face the dual bottlenecks of high computational costs and ghosting artifacts, hindering their widespread deployment. To this end, this study…

Replaced article(s) found for eess.IV. https://arxiv.org/list/eess.IV/new
[1/1]:
- Beyond Feature Mapping GAP: Integrating Real HDRTV Priors for Superior SDRTV-to-HDRTV Conversion
Gang He, Kepeng Xu, Li Xu, Wenxin Yu, Xianyun Wu