# Blur Image Online

Drop your image(s) here

## How to use this tool to Blur Image 🤔

- Click 👆 on the button and choose input image files, or you may drag and drop the image files to the Drop area. You may choose one image 🌁 or multiple images 🌃 🌄 🎇. The chosen images are displayed with size information in the input section.
- You may change the conversion parameters like quality 🎛, background color, etc., using the sliders, or color input buttons.
- Now you can click on the
**Blur Image**operation. This all happens in a lightening speed ⚡. button. This is where the ⛄ magic happens. Your images are sent ➚ to the server for the specified - Once the input images are transformed ✅, they appear in the download section.
- You may click 👆 on the big button to download ↓ all your transformed images.
- There is also a link 🔗 which you can and share it using which you can download the resulting images.

**Disclaimer:**Please be aware that due to limited server storage, converted images will be automatically purged over time for optimal server performance.

## Blur Image Effect

The blur image effect smoothens an image by averaging the pixel values with their neighbors. This effect is often used to reduce noise and detail, creating a softened appearance.

The blurring process involves using a convolution kernel (or filter) that averages the pixel values in a neighborhood. A common kernel used for blurring is the Gaussian kernel or a simple box blur kernel.

### Blurring with a Kernel

A simple 3x3 box blur kernel can be used to average the pixel values. The new value of a pixel is calculated by averaging its value with the values of its neighboring pixels.

An example of a box blur kernel is:

\[ \begin{bmatrix} \frac{1}{9} & \frac{1}{9} & \frac{1}{9} \\ \frac{1}{9} & \frac{1}{9} & \frac{1}{9} \\ \frac{1}{9} & \frac{1}{9} & \frac{1}{9} \end{bmatrix} \]

This kernel averages the values of the pixel and its eight neighbors.

### Example Calculation

Consider a pixel \( P \) and its 3x3 neighborhood:

\[ \begin{bmatrix} P_{11} & P_{12} & P_{13} \\ P_{21} & P & P_{23} \\ P_{31} & P_{32} & P_{33} \end{bmatrix} \]

The new value of pixel \( P' \) is calculated as:

\[ P' = \frac{1}{9} (P_{11} + P_{12} + P_{13} + P_{21} + P + P_{23} + P_{31} + P_{32} + P_{33}) \]

### Applying the Kernel to the Image

The blurring effect is applied to the entire image by convolving the blur kernel with the image. This involves moving the kernel over each pixel in the image, computing the new pixel values, and replacing the old values with the new blurred values.

For a more sophisticated blur effect, a Gaussian blur kernel can be used. A Gaussian blur kernel applies a weighted average where the central pixel has more influence than the surrounding pixels.

An example of a 3x3 Gaussian blur kernel is:

\[ \begin{bmatrix} \frac{1}{16} & \frac{2}{16} & \frac{1}{16} \\ \frac{2}{16} & \frac{4}{16} & \frac{2}{16} \\ \frac{1}{16} & \frac{2}{16} & \frac{1}{16} \end{bmatrix} \]

Blurring an image reduces its sharpness and detail, resulting in a smooth and softened appearance. The process involves averaging pixel values to diminish the prominence of edges and noise.