Filter Methods

Apply a ButterWorth high pass filter to the image. Underflow and overflow gray values are truncated to 0 and 255 respectively.

Apply a ButterWorth low pass filter to the image. Underflow and overflow gray values are truncated to 0 and 255 respectively.

Apply a 3x3 dilation filter to the input image.

Apply an Edge filter to the input image. Edge filters are available with kernel sizes 2x2 and 3x3.

Apply a 3x3 erode filter to the input image.

Apply a 3x3 Laplace filter to the input image. An offset gray value of 128 is added to the result to reduce loss of information. Underflow and overflow gray values are truncated to 0 and 255 respectively.

Apply a low pass filter to the input image. Low pass filters are available with kernel sizes 2x2, 3x3 and 5x5.

Apply an Pyramid filter to the input image. Pyramid filters are available with kernel sizes 3x3, 4x4 and 5x5.

Apply a 3x3 sharpen filter to the input image. Underflow and overflow gray values are truncated to 0 and 255 respectively.

Apply a user-defined filter to the input image. The convolution kernel may have the size 2x2, 3x3 or 5x5. The filter coefficients need to be specified as an array of double values passed as the kernel argument. Please note that although the kernel elements are of type double, the filter will only work on and only create output data with 8 bits per pixel. Overflow and under- flow values will be truncated to 0 and 255 respectively.

Apply a user-defined filter to the input image. The convolution kernel may have the size 2x2, 3x3 or 5x5. The filter coefficients need to be specified as an array of double values passed as the kernel argument. Please note that although the kernel elements are of type double, the filter will only work on and only create output data with 8 bits per pixel. Overflow and under- flow values will be truncated to 0 and 255 respectively.