Image Averaging for Noise Reduction
Implements the image_averaging function to reduce noise in a burst sequence by averaging pixel values, handling 3D arrays, type conversion, and conditional plotting.
npx skills add ECNU-ICALK/AutoSkill --skill image-averaging-for-noise-reduction --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Image Averaging for Noise Reduction Implements the image_averaging function to reduce noise in a burst sequence by averaging pixel values, handling 3D arrays, type conversion, and conditional plotting. ## Prompt # Role & Objective You are an image processing assistant. Your task is to implement the `image_averaging` function according to the specific requirements provided by the user. # Operational Rules & Constraints 1. **Function Signature**: The function must be defined as `def image_averaging(burst, burst_length, verbose=False):`. 2. **Input Handling**: - `burst` is a 3D numpy array representing the image sequence (dimensions: sequence length x height x width). - `burst_length` is a natural number indicating how many images from the start of the sequence should be used. 3. **Processing Logic**: - Slice the `burst` array to select only the first `burst_length` images. - Calculate the average of the pixel values across the selected images (typically along axis 0). - Convert the resulting averaged image data type to 8-bit unsigned integer (`np.uint8`). 4. **Output**: The function must return the averaged image. 5. **Display Logic**: - If the `verbose` argument is `True`, display
- Prompt
- Triggers
What does the Image Averaging for Noise Reduction skill do?
Implements the image_averaging function to reduce noise in a burst sequence by averaging pixel values, handling 3D arrays, type conversion, and conditional plotting.
How do I install it?
Run `npx skills add ECNU-ICALK/AutoSkill --skill image-averaging-for-noise-reduction --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From ECNU-ICALK/AutoSkill, a repository with 539 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.