Content deleted Content added
m Added advantages and disadvatages |
|||
Line 442:
|[[File:Equalized Histogram.svg|thumb|300px|Corresponding histogram (red) and cumulative histogram (black)|center]]
|}
=== Advantages ===
# Better-Looking Images: Histogram equalization makes images look better by making the details clearer and improving their overall appearance.
# Helps Spot Things: It can help computer programs spot important features in images, which is handy for tasks like medical diagnosis or identifying objects.
# Easy to Use: You don't need to be an expert to use histogram equalization. It's straightforward and can be used by beginners in image editing and computer vision.
# Saves Time and Resources: Since it works directly on the image pixels, you don't need extra data or complicated setup, making it efficient for real-time applications and devices with limited computing power.
=== Disadvantages ===
# Can Make Things Look Strange: Sometimes, histogram equalization can make images look odd by exaggerating small details or making textures too prominent, which can be distracting.
# Not Good with Local Details: It might not preserve small details well, especially in areas of the image that have both bright and dark parts.
# Not Customizable Enough: It treats the whole image the same way, which might not work well for images with specific lighting conditions or unique characteristics.
# Tricky to Fine-Tune: Adjusting the settings for histogram equalization can be tricky, and getting it just right often requires a good understanding of image processing.
# Depends on the Task: Its effectiveness depends on what you're using it for. Sometimes, other methods might work better for enhancing images or analyzing them.
== See also ==
| |||