G**Y
发帖数: 33224 | 1 问问？虽然我不懂，但是既然可以改进MRI，离照相也不远了。
这斯太NB了，21岁普林斯顿毕业。
他这个和Candes合作的算法，跟得Fields奖的东西应该是两码事儿，
http://www.wired.com/magazine/2010/02/ff_algorithm/all/1
这个东西本身，基本上又开启了一个新领域呀。有点Shannon的感觉了。 |
T********r
发帖数: 6210 | 2 可以用在侦察卫星里么？
【在 G**Y 的大作中提到】
: 问问？虽然我不懂，但是既然可以改进MRI，离照相也不远了。
: 这斯太NB了，21岁普林斯顿毕业。
: 他这个和Candes合作的算法，跟得Fields奖的东西应该是两码事儿，
: http://www.wired.com/magazine/2010/02/ff_algorithm/all/1
: 这个东西本身，基本上又开启了一个新领域呀。有点Shannon的感觉了。
|
G**Y
发帖数: 33224 | 3 ...
Candès can envision a long list of applications based on what he and his
colleagues have accomplished. He sees, for example, a future in which the
technique is used in more than MRI machines. Digital cameras, he explains,
gather huge amounts of information and then compress the images. But
compression, at least if CS is available, is a gigantic waste. If your
camera is going to record a vast amount of data only to throw away 90
percent of it when you compress, why not just save battery power and memory
and record 90 percent less data in the first place? For digital snapshots of
your kids, battery waste may not matter much; you just plug in and recharge
. “But when the battery is orbiting Jupiter,” Candès says, “it’s a
different story.” Ditto if you want your camera to snap a photo with a
trillion pixels instead of a few million.
The ability to gather meaningful data from tiny samples of information is
also enticing to the military: Enemy communications, for instance, can hop
from frequency to frequency. No existing hardware is fast enough to scan the
full range. But the adversary’s signal, wherever it is, is sparse — built
up from simple signals in some relatively tiny but unknown portion of the
frequency band. That means CS could be used to distinguish enemy chatter on
a random band from crackle. Not surprisingly, Darpa, the Defense Department
’s research arm, is funding CS research.
...
应该可以
【在 T********r 的大作中提到】
: 可以用在侦察卫星里么？
|
N****w
发帖数: 21578 | 4 这玩艺是不是跟白噪声叠加抵消类似阿
【在 T********r 的大作中提到】
: 可以用在侦察卫星里么？
|
N****w
发帖数: 21578 | 5 偶觉得适合低频图像处理
用来磨皮最合适了
高频图像这么搞显然会丢失很多细节
memory
of
recharge
【在 G**Y 的大作中提到】
: ...
: Candès can envision a long list of applications based on what he and his
: colleagues have accomplished. He sees, for example, a future in which the
: technique is used in more than MRI machines. Digital cameras, he explains,
: gather huge amounts of information and then compress the images. But
: compression, at least if CS is available, is a gigantic waste. If your
: camera is going to record a vast amount of data only to throw away 90
: percent of it when you compress, why not just save battery power and memory
: and record 90 percent less data in the first place? For digital snapshots of
: your kids, battery waste may not matter much; you just plug in and recharge
|
G**Y
发帖数: 33224 | 6 不懂呀，最近正在研究。
肯定也不是万能的，
大概就是说如果你要100M的照片，
大概10M的Sensor足够了。
照相这个应用应该是比较简单的，
因为你可以选在sensors放在哪。
而且噪音估计也不是很大。
这里面就觉得Candes很能忽悠，
Tao数学很NB。
【在 N****w 的大作中提到】
: 这玩艺是不是跟白噪声叠加抵消类似阿
|
k******a
发帖数: 2436 | 7 It reconstruct the details by guessing then prove the result is
mathematically optimal under some assumptions/conditions. I am skeptical
about its application in amateur/professional photography since many areas
in a photo can be out of focus.
【在 N****w 的大作中提到】
: 这玩艺是不是跟白噪声叠加抵消类似阿
|
G**Y
发帖数: 33224 | 8 你说的那些更像FF啥的，
他这个可能不太一样。
不够Tao搞调和分析，估计这些东西多少有点联系。
【在 N****w 的大作中提到】
: 偶觉得适合低频图像处理
: 用来磨皮最合适了
: 高频图像这么搞显然会丢失很多细节
:
: memory
: of
: recharge
|
G**Y
发帖数: 33224 | 9 我觉得一个数量级的优化还是可能的。
另外一个paper里，他们大概就是说他们的结果，应该比Shannon, Nyquist啥的还NB，
当然是在特定的假设下。这是摘要：
Conventional approaches to sampling signals or images follow Shannon's
theorem: the sampling rate must be at least twice the maximum frequency
present in the signal (Nyquist rate). In the field of data conversion,
standard analog-to-digital converter (ADC) technology implements the usual
quantized Shannon representation - the signal is uniformly sampled at or
above the Nyquist rate. This article surveys the theory of compressive
sampling, also known as compressed sensing or CS, a novel sensing/sampling
paradigm that goes against the common wisdom in data acquisition. CS theory
asserts that one can recover certain signals and images from far fewer
samples or measurements than traditional methods use.
【在 k******a 的大作中提到】
: It reconstruct the details by guessing then prove the result is
: mathematically optimal under some assumptions/conditions. I am skeptical
: about its application in amateur/professional photography since many areas
: in a photo can be out of focus.
|
k******a
发帖数: 2436 | 10 Now I had time think about it, in fact, the Bayer Filter Demosaic process
can be considered as a simple form of the very problem this method solves.
In that sense the algorithm can help to do camera raw conversion.
Not sure about in camera use due to its performance; maybe in post-
processing only.
This is not the first of such algorithms. A Japanese company does similar
things and they have consumer software for up-sampling and raw-conversion.
【在 k******a 的大作中提到】
: It reconstruct the details by guessing then prove the result is
: mathematically optimal under some assumptions/conditions. I am skeptical
: about its application in amateur/professional photography since many areas
: in a photo can be out of focus.
|
i**e
发帖数: 6810 | 11 那幾張圖是simulation而已，實際效果如何？ |
