Removing Noise

LBitmap::MedianFilter is an effective general function for removing salt-and-pepper noise from any kind of image. You control the strength of the function by specifying the size of the neighborhood (the surrounding pixels used for calculating the median value). This function causes minimal blurring of the image.

The LBitmap::AverageFilter function can be used to remove uniform and Gaussian noise, but there is more blurring of the image than with the median filter. You control the strength of the function by specifying the size of the neighborhood (the surrounding pixels used for calculating the arithmetic mean).

The LBitmap::Add function can be used to average the bitmaps in a list, eliminating random noise contained in the images.

The LBitmap::AddWeighted function can be used to average the bitmaps in a list, eliminating random noise contained in the images. Typically, you would call this function for a series of images taken for the same object at short intervals. This function can eliminate random noise by performing a weighted average

The LBitmap::HighPassFilter function removes low frequency details in a bitmap, resulting in a sharpened image. The effect is opposite to that of the Gaussian Blur filter (LBitmap::GaussianFilter). Unlike the LBitmap::UnsharpMask function, when using this function image noise is not increased.

The LBitmap::GaussianFilter function smoothes or blurs an image by applying a Gaussian filter to each pixel of the bitmap. The amount of blur is determined by the size of the neighborhood used by the function.

The LBitmap::DynamicBinary function converts a bitmap into a black and white image without changing its bits per pixel.

The LBitmap::Deinterlace function deinterlaces video source images by removing the black lines. It proceeds by merging lines in the image together and/or blending them together.

The LBitmap::SmoothEdges function smoothes the edges of a bitmap.

Document/Medical

The LBitmap::Despeckle function is designed specifically for removing specks from 1-bit images, such as FAX transmissions or scanned documents.

The binary filters (for erosion and dilation of black objects) can be used for noise removal. The LBitmap::BinaryFilter function applies directional binary filters. The LBitmap::MaxFilter and LBitmap::MinFilter functions let you control the neighborhood size for erosion or dilation. One noise removal technique with these functions is to create two copies of a bitmap, apply a dilation filter to one of them and an erosion filter to the other, then combine the two using LBitmapBase::Combine with the CB_OP_AVG (averaging) flag.

The LBitmap::AutoBinary function transforms the bitmap into a binary image using a threshold calculated automatically based on statistical features of the bitmap. Best results are obtained with grayscale bitmaps.

Fourier Transforms

Fourier Transforms are useful in removing harmonic noise like:

• Herringbone patterns seen in video images

• Sine wave patterns

• Moiré patterns

• Halftone patterns

• Interference patterns

Fourier Transforms are also useful in removing noise from video signals and CCDs.

Fourier transforms convert an image from one where intensity varies over space into one where the intensity varies with frequency. After an image has been described (transformed) as a series of frequencies and phase, that information can then be analyzed using a power spectrum. The power spectrum is a two-dimensional map that plots the frequency of pixel intensity values. Low frequencies lie close to the origin and high frequencies close to the edges. From the analysis suitable filters can be applied to remove those parts of the image that correspond to the noise in the image. The image can then be retransformed back into the original image, only without the noise.

A Discrete Fourier Transform is a Fourier transform that uses a series which has values that have both a real and an imaginary component. Discrete Fourier transforms reveal periodicities in the data as well as the relative strengths of those periodicities.

Discrete Fourier Transforms can take a long time to compute. To reduce computing time, Fast Fourier Transforms can be used, which reduce the number of computations from 2N2 to 2N Lg N. However, using a Fast Fourier Transform requires that the number of points in the series be a power of 2.

The following functions provide Fourier transform capabilities: