Paper Info
Title
Noise and dynamic range optimal computational imaging.
Abstract
Computational photography techniques overcome limitations of traditional image sensors such as dynamic range and noise. Many computational imaging techniques have been proposed that process image stacks acquired using different exposure, aperture or gain settings, but far less attention has been paid to determining the parameters of the stack automatically. In this paper, we propose a novel computational imaging system that automatically and efficiently computes the optimal number of shots and corresponding exposure times and gains, taking into account characteristics of the scene and sensor. Our technique seamlessly integrates the use of multiple capture for both High Dynamic Range (HDR) imaging and denoising. The acquired images are then aligned, warped and merged in the raw Bayer domain according to a statistical noise model of the sensor to produce an optimal, potentially HDR and denoised image. The result is a fully automatic camera that constantly monitors the scene in front of it and decides how many images are required to capture it, without requiring the user to explicitly switch between different capture modalities.
Year
DOI
Venue
2012
10.1109/ICIP.2012.6467477
ICIP
Keywords
Field
DocType
cameras,image denoising,image sensors,photography,statistical analysis,HDR imaging,automatic camera,computational photography,dynamic range optimal computational imaging,high dynamic range image,image denoising,image sensor,raw Bayer domain,statistical noise model,HDR,SNR,denoising,mobile imaging
Aperture,Noise reduction,Computer vision,Statistical noise,Dynamic range,Image sensor,Computer science,Computational photography,Image noise,Artificial intelligence,High dynamic range
Conference
ISSN
Citations 
PageRank 
1522-4880
2
0.38
References 
Authors
7
3
Name
Order
Citations
PageRank
Kalpana Seshadrinathan194340.70
Sung Hee Park211610.11
Oscar Nestares313412.37