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  2. Kernel (image processing) - Wikipedia

    en.wikipedia.org/wiki/Kernel_(image_processing)

    2D convolution with an M × N kernel requires M × N multiplications for each sample (pixel). If the kernel is separable, then the computation can be reduced to M + N multiplications. Using separable convolutions can significantly decrease the computation by doing 1D convolution twice instead of one 2D convolution. [2]

  3. Convolutional layer - Wikipedia

    en.wikipedia.org/wiki/Convolutional_layer

    In artificial neural networks, a convolutional layer is a type of network layer that applies a convolution operation to the input. Convolutional layers are some of the primary building blocks of convolutional neural networks (CNNs), a class of neural network most commonly applied to images, video, audio, and other data that have the property of uniform translational symmetry.

  4. Gabor filter - Wikipedia

    en.wikipedia.org/wiki/Gabor_filter

    Its impulse response is defined by a sinusoidal wave (a plane wave for 2D Gabor filters) multiplied by a Gaussian function. [6] Because of the multiplication-convolution property (Convolution theorem), the Fourier transform of a Gabor filter's impulse response is the convolution of the Fourier transform of the harmonic function (sinusoidal function) and the Fourier transform of the Gaussian ...

  5. Comparison of raster graphics editors - Wikipedia

    en.wikipedia.org/wiki/Comparison_of_raster...

    Free photo organizer and image editor Renchi Raju, Gilles Caulier 2002: 8.5.0 [4] 2024-11-16 Free GPL-2.0-or-later: Digital Photo Professional: Photo organizer and image editor from Canon Canon Inc. 4.14.0 March 10, 2021: Proprietary: DxO PhotoLab: RAW image editor DxO Labs: October 26, 2017: 7.5 March 1, 2024: €229 Proprietary: Erdas Imagine

  6. Image derivative - Wikipedia

    en.wikipedia.org/wiki/Image_derivative

    Image derivatives can be computed by using small convolution filters of size 2 × 2 or 3 × 3, such as the Laplacian, Sobel, Roberts and Prewitt operators. [1] However, a larger mask will generally give a better approximation of the derivative and examples of such filters are Gaussian derivatives [ 2 ] and Gabor filters . [ 3 ]

  7. Multidimensional discrete convolution - Wikipedia

    en.wikipedia.org/wiki/Multidimensional_discrete...

    This ensures that a two-dimensional convolution will be able to be performed by a one-dimensional convolution operator as the 2D filter has been unwound to a 1D filter with gaps of zeroes separating the filter coefficients. One-Dimensional Filtering Strip after being Unwound. Assuming that some-low pass two-dimensional filter was used, such as:

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