TF_Lecture 4_Remote Sensing Image Enhancement

In the Gaussian stretch procedure, the observed number of pixels from the input image is used to find the target number of pixels of each class.

The standard deviation of a normal distribution is typically denoted by the symbol σ.

Histogram matching is a technique used to stretch the contrast of an image by fitting the histogram to a uniform distribution.

In image quantization, the number of quantization levels determines the number of possible pixel values in the output image.

The probability of each class in the target distribution is calculated using the z-score formula.

For a given continuous transformation function, the condition 2 of the transformation function guarantees the output grey levels will be beyond the range of the input levels.

In Histogram Equalization, the goal is to obtain a histogram in the result that follows a Gaussian distribution.

In the context of Contrast Stretching, reducing the number of levels used in the lookup table can result in an unsatisfactory output.

Histogram Matching is a technique used to adjust the brightness of an image while maintaining its relative brightness.

The probability density function (PDF) of the continuous random variables in Histogram Equalization can be used to model discrete random variables.

In histogram equalization, the transformation function is a probability density function (PDF).

Histogram equalization is a technique used to compress the dynamic range of an image, reducing the number of gray levels.

The goal of histogram equalization is to produce an output image that has a Gaussian distribution of gray levels.

Histogram equalization is a technique used for image quantization, where the number of gray levels is reduced.

The transformation function in histogram equalization is a linear function that maps the input image values to output values.

In histogram matching, the goal is to replace zk with rk.

The standard deviation of a normal distribution is typically denoted by the symbol μ.

In image quantization, the number of quantization levels determines the total number of pixels in the output image.

Histogram matching is a technique used to reduce the contrast of an image by fitting the histogram to a normal distribution.

The probability of each class in the target distribution is calculated using the histogram equalization formula.