Histogram equalization in Computer Vision - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is histogram equalization in image processing?

Histogram equalization is a technique to improve the contrast of an image by spreading out the most frequent intensity values. It makes dark areas lighter and light areas darker to use the full range of pixel values.

beginner

Why do we use histogram equalization on images?

We use histogram equalization to make details in an image more visible by increasing contrast, especially when the image is too dark or too bright and details are hard to see.

intermediate

What is the main step in histogram equalization?

The main step is to compute the cumulative distribution function (CDF) of the image's pixel intensities and then map the old pixel values to new ones based on this CDF to spread out the intensities evenly.

intermediate

How does histogram equalization affect the histogram of an image?

It changes the histogram from being concentrated in a small range to being more spread out across all intensity levels, making the histogram more uniform.

advanced

Can histogram equalization be applied to color images directly? Why or why not?

Applying histogram equalization directly to each color channel can distort colors. Instead, it is better to convert the image to a color space like HSV or LAB and apply equalization only to the brightness channel.

What does histogram equalization primarily improve in an image?

A Image size

B Contrast

C Color saturation

D Resolution

Histogram equalization improves the contrast by redistributing pixel intensities.

Which function is used to map old pixel values to new ones in histogram equalization?

A Probability density function (PDF)

B Gradient function

C Fourier transform

D Cumulative distribution function (CDF)

The cumulative distribution function (CDF) is used to map pixel values in histogram equalization.

What happens to the histogram of an image after histogram equalization?

A It becomes more uniform

B It becomes more concentrated

C It shifts to the left

D It disappears

Histogram equalization spreads out pixel intensities, making the histogram more uniform.

Why should histogram equalization be applied to the brightness channel in color images?

A To prevent color distortion

B To increase resolution

C To avoid changing image size

D To reduce noise

Applying equalization only to brightness avoids distorting the colors.

Histogram equalization is most useful when an image is:

A High resolution

B Already very bright

C Low contrast

D Black and white only

Histogram equalization helps improve images with low contrast.

Explain how histogram equalization works to improve image contrast.

Think about how pixel brightness values are redistributed.

Describe the challenges of applying histogram equalization to color images and how to address them.

Consider how color and brightness are separated in different color spaces.