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Histogram Equalization

Computer Vision

Medium

Histogram equalization improves image contrast by redistributing pixel intensities so they span the full [0, 255] range more uniformly. It is one of the most commonly used image enhancement techniques.

Given a 2D grayscale image with integer pixel values in [0, 255], apply histogram equalization and return the transformed image.

Algorithm

Compute the histogram: count the frequency of each intensity value (0 through 255).
Compute the cumulative distribution function (CDF): cdf[i] = sum of hist[0] through hist[i].
Find cdf_min, the smallest non-zero value in the CDF (the CDF of the darkest pixel that actually appears).
Map each pixel value v to a new value using:

new\_val = round\left(\frac{cdf[v] - cdf_{min}}{total\_pixels - cdf_{min}} \times 255\right)

If all pixels have the same value (total_pixels equals cdf_min), map every pixel to 0.

Loading visualization...

Examples

Input: image = [[0, 1], [2, 3]]

Output: [[0, 85], [170, 255]]

Explanation: Four equally frequent intensities are spread across the full output range.

Input: image = [[100, 100], [100, 100]]

Output: [[0, 0], [0, 0]]

Hint 1

Build a 256-bin histogram, then convert it into a cumulative count array.

Hint 2

Use the first positive cumulative count as the lower endpoint of the mapping.

Requirements

Build a 256-bin histogram and its cumulative sum
Use round() (not floor or ceil) when mapping to new values
Handle the edge case where all pixels are the same value

Constraints

Image has at least one pixel
Pixel values are integers in [0, 255]
Return a 2D list of integers with the same shape as input
Time limit: 300 ms

Try Similar Problems

Image Histogram Color To Grayscale Gaussian Blur Kernel Conv2d Image Filtering Sobel Edge Detection

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Histogram Equalization

Computer Vision

Medium

Given a 2D grayscale image with integer pixel values in [0, 255], apply histogram equalization and return the transformed image.

Algorithm

Compute the histogram: count the frequency of each intensity value (0 through 255).
Compute the cumulative distribution function (CDF): cdf[i] = sum of hist[0] through hist[i].
Find cdf_min, the smallest non-zero value in the CDF (the CDF of the darkest pixel that actually appears).
Map each pixel value v to a new value using:

new\_val = round\left(\frac{cdf[v] - cdf_{min}}{total\_pixels - cdf_{min}} \times 255\right)

If all pixels have the same value (total_pixels equals cdf_min), map every pixel to 0.

Loading visualization...

Examples

Input: image = [[0, 1], [2, 3]]

Output: [[0, 85], [170, 255]]

Explanation: Four equally frequent intensities are spread across the full output range.

Input: image = [[100, 100], [100, 100]]

Output: [[0, 0], [0, 0]]

Hint 1

Build a 256-bin histogram, then convert it into a cumulative count array.

Hint 2

Use the first positive cumulative count as the lower endpoint of the mapping.

Requirements

Build a 256-bin histogram and its cumulative sum
Use round() (not floor or ceil) when mapping to new values
Handle the edge case where all pixels are the same value

Constraints

Image has at least one pixel
Pixel values are integers in [0, 255]
Return a 2D list of integers with the same shape as input
Time limit: 300 ms

Try Similar Problems

Image Histogram Color To Grayscale Gaussian Blur Kernel Conv2d Image Filtering Sobel Edge Detection

Case 1

Case 2

image =

Accepts: array

Given a 2D grayscale image with integer pixel values in [0, 255], apply histogram equalization and return the transformed image.

Algorithm

Compute the histogram: count the frequency of each intensity value (0 through 255).
Compute the cumulative distribution function (CDF): cdf[i] = sum of hist[0] through hist[i].
Find cdf_min, the smallest non-zero value in the CDF (the CDF of the darkest pixel that actually appears).
Map each pixel value v to a new value using:

new\_val = round\left(\frac{cdf[v] - cdf_{min}}{total\_pixels - cdf_{min}} \times 255\right)

If all pixels have the same value (total_pixels equals cdf_min), map every pixel to 0.