The two domains of non-data adaptive transform domain filtering techniques are the spatial frequency domain and the wavelet domain. Nevertheless, in some applications it may be difficult to obtain noise-free training data.
#DENOISE IMAGE ONLINE WINDOWS#
However, because they use sliding windows and require a noise-free data sample or at least two frames from the same scene, their main drawback is high computational complexity. The assumptions regarding the distinction between image and noise still apply to these two types of data-adaptive algorithms. Among them, the ICA approach has been effectively used for degaussing non-Gaussian data. Independent Component Analysis (ICA) and PCA functions are used as transform techniques on the provided noisy images. The selected basis transform functions, which can be data-adaptive or non-data-adaptive, can be used to further categories the transform domain filtering techniques. Unlike spatial domain filtering techniques, transform domain filtering techniques first transform the noisy input image into another domain and then apply an image denoising technique to the transformed image according to the different characteristics of the input image and its noise (larger coefficients denote the high frequency part, such as the details or edges of the image, while smaller coefficients denote the noise).
The properties of image information and noise are different in the transform domain, an observation that is exploited by transform domain approaches. Originally derived from the Fourier transform, transform domain approaches have grown to include a number of techniques, including the cosine transform, wavelet domain methods, block matching and 3D filtering (BM3D), among others. The first approaches to image denoising were in the spatial domain, while the most recent methods are in the transform domain.
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