突破传统：麻省理工计算摄影技术概览与应用

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"这篇来自麻省理工学院教授Ramesh Raskar、Jack Tumblin等人在EUROGRAPHICS 2006年发布的State-of-the-Art报告《计算摄影》是一篇极具价值的论文。计算摄影作为一种结合了丰富的计算能力、数字传感器、现代光学技术、驱动元件、探头以及智能光源的新兴领域，突破了传统胶片相机的局限，催生了许多创新的成像应用。报告中强调了诸如无限动态范围、可变焦距、分辨率提升、深度景深控制、形状提示、表面反光分析以及光线控制等前所未有的特性。文章深入探讨了计算摄影的计算机技术手段，包括在拍摄过程中对图像参数的实时调整，以及从间接测量数据进行复杂重构的方法。它为计算机图形学和计算机视觉提供了一个实用指南，旨在通过图像捕捉和处理技术，创造出引人入胜的图片，同时也支持场景属性的提取，对于数字艺术家来说是一份宝贵的资源。报告中分享了大量的实践案例，展示了如何将理论转化为实际操作，许多计算摄影的概念对于当时的数字艺术家来说是新颖且富有挑战性的。例如，计算摄影允许照片的部分是静态照片，部分则是动态视频效果的融合，这为摄影师和艺术家打开了全新的创意空间。这篇文章不仅阐述了计算摄影的核心理念，还对未来可能的发展方向进行了前瞻性思考，对于所有关注图像处理和先进成像技术的人来说，都是一份不可多得的研究资料。"

Raskar and Tumblin/Computational Photography

Coded Photography is a notion of an 'out-of-the-box'

photographic method, in which individual (ray) samples

or data sets are not comprehensible as ‘images’ without

further decoding, re-binning or reconstruction. For

example, a wrap around view is built from images taken

with multiple centers of projection but by taking only a

few pixels from each input image. Some other examples

include confocal images and coded aperture images.

We may be converging on a new, much more capable

'box' of parameters in computational photography that

we don't yet recognize; there is still quite a bit of

innovation to come!

In the rest of the STAR, we survey recent techniques

that exploit exposure, focus and active illumination.

3 High Dynamic Range

3.1 Multiple Exposures

One approach of capturing high dynamic range scenes is

to capture multiple images using different exposures,

and then merge these images. The basic idea is that

when high exposures are used, dark regions are well

imaged but bright regions are saturated. On the other

hand, when low exposures are used, dark regions are too

dark but bright regions are well imaged. If exposure

varies and multiple pictures are taken of the same scene,

value of a pixel can be taken from those images where

it's neither too dark nor saturated. This type of approach

is often referred to as exposure bracketing, and has been

widely adopted [Morimura 1993, Burt and Kolczynski

1993,Madden 1993,Tsai 1994]. Imaging devices usually

contain nonlinearities, where pixel values are

nonlinearly related to the brightness values in the scene.

Some authors have proposed to use images acquired

under different exposures to estimate the radiometric

response function of an imaging device, and use the

estimated response function to process the images before

merging them [Mann and Picard 1995, Debevec and

Malik 1997, Mitsunaga and Nayar 1999.]

3.2 Sensor Design

At the sensor level, various approaches have also been

proposed for high dynamic range imaging. One type of

approach is to use multiple sensing elements with

different sensitivities within each cell [Street 1998,

Handy 1986, Wen 1989, Hamazaki 1996]. Multiple

measurements are made from the sensing elements, and

they are combined on-chip before a high dynamic range

image is read out from the chip. Spatial sampling rate is

lowered in these sensing devices, and spatial resolution

is sacrificed. Another type of approach is to adjust the

well capacity of the sensing elements during

photocurrent integration [Knight 1983, Sayag 1990,

Decker 1998] but this gives higher noise. A different

approach is proposed by [Brajovic and Kanade 1996],

where the time it takes to reach saturation is measured,

by a computation element attached to each sensing

element. This time encodes high dynamic range

information, as it is inversely proportional to the

brightness at each pixel. Logarithmic sensors [Scheffer

et al 2000] have also been proposed to increase the

dynamic range. Brightside exploits the interline transfer

of a charge coupled device (CCD) based camera to

capture two exposures during a single mechanical

shutter timing.

High dynamic range sensor design is in progress, but the

implementation is usually costly. A rather novel and

flexible approach is proposed by [Nayar and Mitsunaga

2000, Narasimhan and Nayar 2005], where exposures

vary across space of the imager. A pattern with varying

sensitivities is applied to the pixel array. It resembles the

Bayer pattern in color imaging, but the sampling is made

along the exposure instead of wavelength. The particular

form of the sensitivity pattern, and the way of

implementing it, are both quite flexible. One way of

implementing it is to place a mask with cells of varying

optical transparencies in front of the sensing array. Here,

just as in Bayer mosaic, spatial resolution is sacrificed to

some extent and aliasing can occur. Measurements

under different exposures (sensitivities) are spatially

interpolated, and combined into a high dynamic range

image.

4 Aperture and Focus

Several concepts in exploiting focus and aperture

parameters can be understood by considering the 4D

lightfields transfer via lens and its 2D, 3D or 4D

projection recorded on the image sensor.

Defocus Video Matting

Video matting is the process of recovering a high-

quality alpha matte and foreground from a video

sequence. Common approaches require either a known

background (e.g., a blue screen) or extensive user

interaction (e.g., to specify known foreground and

background elements). The matting problem is generally

under-constrained, unless additional information is

recorded at the time of capture. McGuire et. al. have

proposed a novel, fully autonomous method for pulling

a matte using multiple synchronized video cameras that

share the center of projection but differ in their plane of

focus [McGuire et. al 2005]. The multi-camera data

stream over-constrains the problem and the solution is

obtained by directly minimizing the error in filter-based

image formation equations. Their system solves the fully

dynamic video matting problem without user assistance:

both the foreground and background may be high

frequency and have dynamic content, the foreground

may resemble the background, and the scene may be lit

by natural (as opposed to polarized or collimated)

illumination. The authors capture 3 synchronized video

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