most citedLearning to Refocus with Video Diffusion Models

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cs.CV2026

Dark3R: Learning Structure from Motion in the Dark

Andrew Y Guo, Anagh Malik, SaiKiran Tedla +7

We introduce Dark3R, a framework for structure from motion in the dark that operates directly on raw images with signal-to-noise ratios (SNRs) below dB -- a regime where conve…

cs.CV20251 cited

Learning to Refocus with Video Diffusion Models

SaiKiran Tedla, Zhoutong Zhang, Xuaner Zhang +1

Focus is a cornerstone of photography, yet autofocus systems often fail to capture the intended subject, and users frequently wish to adjust focus after capture. We introduce a nov…

cs.CV20251 cited

Generating the Past, Present and Future from a Motion-Blurred Image

SaiKiran Tedla, Kelly Zhu, Trevor Canham +4

We seek to answer the question: what can a motion-blurred image reveal about a scene's past, present, and future? Although motion blur obscures image details and degrades visual qu…

cs.CV2025

Towards Imperceptible Watermarking Via Environment Illumination for Consumer Cameras

Hodaka Kawachi, Tomoya Nakamura, Hiroaki Santo +4

This paper introduces a method for using LED-based environmental lighting to produce visually imperceptible watermarks for consumer cameras. Our approach optimizes an LED light sou…

cs.CV2025

Improved Mapping Between Illuminations and Sensors for RAW Images

Abhijith Punnappurath, Luxi Zhao, Hoang Le +3

RAW images are unprocessed camera sensor output with sensor-specific RGB values based on the sensor's color filter spectral sensitivities. RAW images also incur strong color casts…

cs.CV2025

Multispectral Demosaicing via Dual Cameras

SaiKiran Tedla, Junyong Lee, Beixuan Yang +2

Multispectral (MS) images capture detailed scene information across a wide range of spectral bands, making them invaluable for applications requiring rich spectral data. Integratin…