PadmanabanVarma: Difference between revisions
imported>Student2016 Created page with '== Introduction == Although camera technology is steadily advancing year-after-year, one of the problems that continues to plague photography is that of low-light noise. With t…' |
imported>Student2016 |
||
| Line 1: | Line 1: | ||
== Introduction == | == Introduction == | ||
Camera technology has been advancing at a fast pace, from giving everyone access to a high-quality cameras in their smartphones to looking beyond 2D images and being able to capture depth information. However, one of the persistent problems that still remains is being able to capture high quality images in low light conditions. As we often see with the images we capture at night with our smart phones, they are usually poor quality and often corrupted by noise that looks like speckles in the image. Specifically, these images are dominated by Poisson shot noise that is inherent to the distribution of photons that are recorded by camera sensors. This photon noise is usually too small relative to the number of photons captured in daylight/bright-light settings and therefore not noticeable. However as photon count drops in dark settings, this noise begins to dominate image formation. | |||
Image denoising is a popular problem that has been studied extensively in the past as a signal denoising problem. However, one of the major drawbacks of these processes is that they focus almost solely on Gaussian noise in images. Gaussian noise is inherently different from Poisson noise in that it is easier to "average out". As described in the next section, there are some methods that look at removing non-Gaussian noise as well, but these methods are very complex and computationally expensive. | |||
We | Our objective in this project is to find a simple and inexpensive method to be able to denoise existing and new low-light images. We approach this problem via a machine learning perspective, specifically linear regression, to learn a parameter that maps the noisy values of a patch in the image to its center pixel value. Once this parameter is learned, it is easy to use it to denoise previously captured images. Moreover, since the approach denoises a patch at a time, it can be applied to images of varying sizes. With this formulation, the denoising only relies on vector-vector multiplications, which can can be computed efficiently and quickly. | ||
== References == | == References == | ||
dkfjsf | dkfjsf | ||
Revision as of 22:50, 12 December 2016
Introduction
Camera technology has been advancing at a fast pace, from giving everyone access to a high-quality cameras in their smartphones to looking beyond 2D images and being able to capture depth information. However, one of the persistent problems that still remains is being able to capture high quality images in low light conditions. As we often see with the images we capture at night with our smart phones, they are usually poor quality and often corrupted by noise that looks like speckles in the image. Specifically, these images are dominated by Poisson shot noise that is inherent to the distribution of photons that are recorded by camera sensors. This photon noise is usually too small relative to the number of photons captured in daylight/bright-light settings and therefore not noticeable. However as photon count drops in dark settings, this noise begins to dominate image formation.
Image denoising is a popular problem that has been studied extensively in the past as a signal denoising problem. However, one of the major drawbacks of these processes is that they focus almost solely on Gaussian noise in images. Gaussian noise is inherently different from Poisson noise in that it is easier to "average out". As described in the next section, there are some methods that look at removing non-Gaussian noise as well, but these methods are very complex and computationally expensive.
Our objective in this project is to find a simple and inexpensive method to be able to denoise existing and new low-light images. We approach this problem via a machine learning perspective, specifically linear regression, to learn a parameter that maps the noisy values of a patch in the image to its center pixel value. Once this parameter is learned, it is easy to use it to denoise previously captured images. Moreover, since the approach denoises a patch at a time, it can be applied to images of varying sizes. With this formulation, the denoising only relies on vector-vector multiplications, which can can be computed efficiently and quickly.
References
dkfjsf