Image decomposition is carried out using an algorithm to analyze the «internal» contrast of groups of pixels (2x2=4 pixels on the first level, 4x4=16 pixels on the second level. RawTherapee uses wavelets in various tools, and in this one in particular it uses the Daubechies wavelet, to decompose the elements of the image into the components of the L*a*b* color space ( L*, a* and b*). Graphic rendering of a Daubechies wavelet Therefore, using wavelets offers more precision when analyzing the data.
WAVELET DENOISE FULL
Broadly speaking, the main difference for two-dimensional images is that in the Wavelet Transform the data being analyzed is represented as the frequencies present at the pixel level of the image, whereas in the standard Fourier Transform the data represents the frequencies present in the full image. The Wavelet Transform, which is similar to a Fourier Transform, represents data as combinations of known and predefined waves (the frequencies), so that the result is as close as possible to the original data. Later, this word was adapted to English changing onde by wave, leading to wavelet. The wavelets term was introduced in the early 1980s by French physicists Jean Morlet and Alex Grossman: they used the French word ondelette, which means small wave. It allows you to split images into different levels of detail so that you can work on the level that interests you.
WAVELET DENOISE SERIES
The tool is organized around a general Wavelet Settings module followed by a series of modules which can activated or deactivated to perform specific tasks.Ī Wavelet, or more precisely a Wavelet Transform, is a complex mathematical function which is very useful in image processing. The capabilities are almost limitless, but you will only be able to use them properly if you have a good understanding of the underlying principles and operation of the various tools, so please read on! It can also be used to great effect in landscape photography to remove noise in skies, compress the dynamic range while at the same time preserving the details, reduce the noise, remove color casts in shadows, and create interesting luminosity effects. It can be used for any sort of image but its unique capabilities make it particularly suitable for portraits, macro photography, astro-photography etc., where selective control over fine detail is important. It allows you to work on different levels of detail to produce subtle contrast and color effects, remove noise or defects in the image without sacrificing overall detail, or work on the color and luminance of the image without introducing artifacts. However, it is most useful when it is used to complete or refine processing operations carried out in other parts of RawTherapee. It has most of the functions necessary for processing photographs from start to finish with the exception of certain tasks such as interpolation or color management. The Wavelet Levels tool is extensive and its underlying algorithms are complex. 13.2.2 Compression Method: Tone Mapping.13.2 Residual Image Contrast Compression.13.1 Shadows/highlights of the residual image.10.6 Example (modifying noise reduction and edge sharpness).9.1.1 Link with Edge Sharpness' Strength.
5.3 Attenuation and selectivity in contrast changes.3.2 Contrast by Detail Levels vs Wavelet Levels.
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