NOISE FILTER ALGORITHM. FILTER ALGORITHM
Noise Filter Algorithm. Mains Rf Filter.
Noise Filter Algorithm
- Noise reduction is the process of removing noise from a signal. Noise reduction techniques are conceptually very similar regardless of the signal being processed, however a priori knowledge of the characteristics of an expected signal can mean the implementations of these techniques vary greatly
- (Noise Filtering) Cleaning the signal (AC) from any interference.
- In mathematics, computer science, and related subjects, an algorithm is an effective method for solving a problem expressed as a finite sequence of steps. Algorithms are used for calculation, data processing, and many other fields.
- A process or set of rules to be followed in calculations or other problem-solving operations, esp. by a computer
- a precise rule (or set of rules) specifying how to solve some problem
GPS Signal Offset Detection and Noise Strength Estimation in a Parallel Kalman Filter Algorithm
This is a AIR FORCE INST OF TECH WRIGHT-PATTERSONAFB OH SCHOOL OF ENGINEERING report procured by the Pentagon and made available for public release. It has been reproduced in the best form available to the Pentagon. It is not spiral-bound, but rather assembled with Velobinding in a soft, white linen cover. The Storming Media report number is A377163. The abstract provided by the Pentagon follows: Measurements from Global Positioning System (GPS) satellites are subject to corruption by signal interference and induced offsets. This thesis presents two independent algorithms to ensure the navigation system remains uncorrupted by these possible GPS failures. The first is a parameter estimation algorithm that estimates the measurement noise variance of each satellite. A redundant measurement differencing (RMD) technique provides direct observability of the differenced white measurement noise samples. The variance of the noise process is estimated and provided to the second algorithm, a parallel Kalman filter structure, which then adapts to changes in the real-world measurement noise strength. The parallel Kalman filter structure detects and isolates signal offsets in individual GPS satellites. The offset detection algorithm calculates test statistics on each of the filters and makes decisions on whether to remove satellites from the solution based on these statistics. The two algorithms contain several user-defined parameters that have significant effects when adjusted. The various effects of parameter variation are described and a parameter set is chosen at which to evaluate the algorithms. The combined algorithm performs quite well in computer simulations.
Noise Reduction ISO 1600
Testing different techniques to cleaning up noise in an ISO1600 shot
A little background on the 3 Xposure/Photoshop combo:
Took the original RAW file and processed it in ACR with 3 different exposure settings sending it to Photoshop and converting to LAB. After that, I adjust the luminance channel in each exposure and reduced and expanded each image (image size) with two different variables (keeping my middle exposure untouched) and minimal Noise Ninja on each exposure. Saving each one out as DNGs. Then I took the three images, combined them into photomatix and then saved it out as a TIFF file. Opened the TIFF in photoshop, then did a high pass filter on it after applying Noise Ninja on the image again.
The results are pretty good (a little watercoloured looking - kind of reminds me of how Nikon high ISO images look like).
This isn't a practical workflow for most, but it's more of a proof of concept that it can be done, and I'm curious if Nikon's in sensor noise reduction algorithms might be trying a similar approach to what I've done to reduce noise.
Automated Noise Reduction With NeatImage
This image is also filtered by NeatImage, but NeatImage was tweaked manually, in this case.
This image was produced using a semi-automatic workflow, pausing at the noise reduction step and allowing the user to intervene with NeatImage to optimize the noise reduction settings.
Neat image initially made a poor choice to profile the image's noise. It chose one of the lighter areas of the image background, which did not exhibit nearly as much noise as the darker areas. Even with the fine tuning, this was not adequate to show the real amount of noise in the image. Manually selecting a darker area (in this case, the bird's chest did not contain much detail, so was acceptable to build a profile from) helped the noise reduction algorithm tremendously. A few adjustments to the sliders to bring down the speckling and the image is about as clear as it can possibly be.
Noise reduction is a tricky process, especially with adaptive filters like NeatImage, and will require human intervention to get the best results possible.
noise filter algorithm
Additive noise is ubiquitous in acoustics environments and can affect the intelligibility and quality of speech signals. Therefore, a so-called noise reduction algorithm is required to mitigate the effect of the noise that is picked up by the microphones. This work proposes a general framework in the time domain for the single and multiple microphone cases, from which it is very convenient to derive, study, and analyze all kind of optimal noise reduction filters. Not only that all known algorithms can be deduced from this approach, shedding more light on how they function, but new ones can be discovered as well.
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