Computational Flash Photography through Intrinsics
- URL: http://arxiv.org/abs/2306.06089v1
- Date: Fri, 9 Jun 2023 17:51:20 GMT
- Title: Computational Flash Photography through Intrinsics
- Authors: Sepideh Sarajian Maralan, Chris Careaga, Ya\u{g}{\i}z Aksoy
- Abstract summary: We study the computational control of the flash light in photographs taken with or without flash.
We present a physically motivated intrinsic formulation for flash photograph formation and develop flash decomposition and generation methods.
- Score: 0.0
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: Flash is an essential tool as it often serves as the sole controllable light
source in everyday photography. However, the use of flash is a binary decision
at the time a photograph is captured with limited control over its
characteristics such as strength or color. In this work, we study the
computational control of the flash light in photographs taken with or without
flash. We present a physically motivated intrinsic formulation for flash
photograph formation and develop flash decomposition and generation methods for
flash and no-flash photographs, respectively. We demonstrate that our intrinsic
formulation outperforms alternatives in the literature and allows us to
computationally control flash in in-the-wild images.
Related papers
- Tracking and triangulating firefly flashes in field recordings [0.0]
I provide a training dataset and trained neural networks for reliable flash classification.
This robust tracking enables a new calibration-free method for the 3D reconstruction of flash occurrences from stereoscopic 360-degree videos.
arXiv Detail & Related papers (2024-10-25T19:07:55Z) - Zero-Reference Low-Light Enhancement via Physical Quadruple Priors [58.77377454210244]
We propose a new zero-reference low-light enhancement framework trainable solely with normal light images.
This framework is able to restore our illumination-invariant prior back to images, automatically achieving low-light enhancement.
arXiv Detail & Related papers (2024-03-19T17:36:28Z) - WildLight: In-the-wild Inverse Rendering with a Flashlight [77.31815397135381]
We propose a practical photometric solution for in-the-wild inverse rendering under unknown ambient lighting.
Our system recovers scene geometry and reflectance using only multi-view images captured by a smartphone.
We demonstrate by extensive experiments that our method is easy to implement, casual to set up, and consistently outperforms existing in-the-wild inverse rendering techniques.
arXiv Detail & Related papers (2023-03-24T17:59:56Z) - Robust Reflection Removal with Flash-only Cues in the Wild [88.13531903652526]
We propose a reflection-free cue for robust reflection removal from a pair of flash and ambient (no-flash) images.
Our model outperforms state-of-the-art reflection removal approaches by more than 5.23dB in PSNR.
We extend our approach to handheld photography to address the misalignment between the flash and no-flash pair.
arXiv Detail & Related papers (2022-11-05T14:09:10Z) - CuDi: Curve Distillation for Efficient and Controllable Exposure
Adjustment [86.97592472794724]
We present Curve Distillation, CuDi, for efficient and controllable exposure adjustment without the requirement of paired or unpaired data.
Our method inherits the zero-reference learning and curve-based framework from an effective low-light image enhancement method, Zero-DCE.
We show that our method is appealing for its fast, robust, and flexible performance, outperforming state-of-the-art methods in real scenes.
arXiv Detail & Related papers (2022-07-28T17:53:46Z) - Seeing Far in the Dark with Patterned Flash [5.540878289831889]
We propose a new flash technique, named patterned flash'', for flash imaging at a long distance.
Patterned flash concentrates optical power into a dot array.
We develop a joint image reconstruction and depth estimation algorithm with a convolutional neural network.
arXiv Detail & Related papers (2022-07-25T23:16:50Z) - Robust Reflection Removal with Reflection-free Flash-only Cues [52.46297802064146]
We propose a reflection-free cue for robust reflection removal from a pair of flash and ambient (no-flash) images.
Our model outperforms state-of-the-art reflection removal approaches by more than 5.23dB in PSNR, 0.04 in SSIM, and 0.068 in LPIPS.
arXiv Detail & Related papers (2021-03-07T05:27:43Z) - Deep Denoising of Flash and No-Flash Pairs for Photography in Low-Light
Environments [51.74566709730618]
We introduce a neural network-based method to denoise pairs of images taken in quick succession, with and without a flash, in low-light environments.
Our goal is to produce a high-quality rendering of the scene that preserves the color and mood from the ambient illumination of the noisy no-flash image.
arXiv Detail & Related papers (2020-12-09T15:41:16Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.