Read the Intensity Distribution
The faded photograph looks flat although no pixel appears completely black.
Read the Intensity Distribution explained visually
The faded photograph looks flat although no pixel appears completely black. English captions are optional and start off. The complete transcript follows the written lesson.
What you will learn
The faded photograph looks flat although no pixel appears completely black.
A practical question
An archivist brings in a faded photograph. It looks flat, and the histogram has a narrow pile of gray values. What does that pile mean? A histogram counts how many pixels have each intensity. It can tell us which values are common and whether the ends of the range are used. It cannot tell us where those pixels lie.
The useful idea
Imagine moving every dark patch in the photograph to a different location while keeping the same number of dark pixels. The histogram would stay the same, although the picture would be very different. A peak might come from the background, a person's clothing, or a large shadow. We need the image beside the plot to identify it.
Work through the example
Compare two versions of a simple scene with the dark areas rearranged. Their intensity counts can match even when the subject no longer does. This is why a histogram can guide a display adjustment but cannot decide whether the important face or object has been preserved. Inspect both the whole scene and a region that matters.
Limits and next step
Keep the unmodified scan for archival work. If we choose to stretch a narrow range of values for viewing, we should record exactly how we map the old values to new ones. We'll design one such mapping next.
Worked example
Compare two images with the same histogram but different placement of dark regions.
Quick check: test your understanding
Try it yourself: In the situation described above, explain the method and one reason its output might be misleading.
Check the reasoning
A histogram counts pixels at each intensity but discards where those pixels lie. Use the original picture alongside the histogram to understand the scene.
What is the main idea behind read the intensity distribution?
A histogram counts pixels at each intensity but discards where those pixels lie.
How is it used in the example?
Compare two images with the same histogram but different placement of dark regions.
What limitation should be checked?
Use the original picture alongside the histogram to understand the scene.
Further reading
Digital Image Processing, 4th edition — Rafael C. Gonzalez and Richard E. Woods.
Original ToolsInVision teaching examples. Editorial approach · Suggest a correction
Accessible referenceLesson 21 transcript210 words · Video transcript · Matches the published narration and captionsOpen transcript
An archivist brings in a faded photograph. It looks flat, and the histogram has a narrow pile of gray values. What does that pile mean? A histogram counts how many pixels have each intensity. It can tell us which values are common and whether the ends of the range are used. It cannot tell us where those pixels lie.
Imagine moving every dark patch in the photograph to a different location while keeping the same number of dark pixels. The histogram would stay the same, although the picture would be very different. A peak might come from the background, a person's clothing, or a large shadow. We need the image beside the plot to identify it.
Compare two versions of a simple scene with the dark areas rearranged. Their intensity counts can match even when the subject no longer does. This is why a histogram can guide a display adjustment but cannot decide whether the important face or object has been preserved. Inspect both the whole scene and a region that matters.
Keep the unmodified scan for archival work. If we choose to stretch a narrow range of values for viewing, we should record exactly how we map the old values to new ones. We'll design one such mapping next.