Sharpen Image Online

Choose Image File(s)

Or
Drop your image(s) here

How to use this tool to Sharpen Image 🤔

Click 👆 on the Choose File(s) 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 Submit button. This is where the ⛄ magic happens. Your images are sent ➚ to the server for the specified Sharpen Image operation. This all happens in a lightening speed ⚡.
Once the input images are transformed ✅, they appear in the download section.
You may click 👆 on the big Download button to download ↓ all your transformed images.
There is also a link 🔗 which you can Copy 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.

Sharpen Image Effect

The sharpen image effect enhances the edges and fine details of an image by increasing the contrast between adjacent pixels. This effect is often used to make images appear clearer and more defined.

The sharpening process involves using a convolution kernel (or filter) that emphasizes the high-frequency components of the image. A common kernel used for sharpening is the Laplacian kernel or an unsharp mask.

Sharpening with a Kernel

The most common sharpening kernel is a 3x3 matrix applied to each pixel and its surrounding pixels. The new value of a pixel is calculated by multiplying each neighboring pixel by a corresponding value in the kernel and summing the results.

An example of a sharpening kernel is:

\[ \begin{bmatrix} 0 & -1 & 0 \\ -1 & 5 & -1 \\ 0 & -1 & 0 \end{bmatrix} \]

This kernel emphasizes the central pixel while subtracting the values of the neighboring pixels to increase contrast.

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:

\[ P' = (0 \times P_{11}) + (-1 \times P_{12}) + (0 \times P_{13}) + (-1 \times P_{21}) + (5 \times P) + (-1 \times P_{23}) + (0 \times P_{31}) + (-1 \times P_{32}) + (0 \times P_{33}) \]

\[ P' = 5P - P_{12} - P_{21} - P_{23} - P_{32} \]

Applying the Kernel to the Image

The sharpening effect is applied to the entire image by convolving the sharpening 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 sharpened values.

Sharpening an image enhances its details and edges, making it appear more defined and crisp. The process involves adjusting the pixel values based on their neighbors to increase contrast and highlight fine details.

{ "operation": "sharpen", "title": "Sharpen Image", "description": "You can use this Sharpen Image tool, to sharpen the edges of image(s). You can do for bulk number of images in one go, and also control the level of sharpness.", "multiple": true, "controls": [ { "type": "range", "name": "sigma", "label": "Level of sharpness", "range": { "min": 1, "max": 10, "value": 2 }, "desc": "More the level of sharpness, more is the image sharpened." } ], "content": "<h2>Sharpen Image Effect</h2>\n<p>The sharpen image effect enhances the edges and fine details of an image by increasing the contrast between adjacent pixels. This effect is often used to make images appear clearer and more defined.</p>\n<p>The sharpening process involves using a convolution kernel (or filter) that emphasizes the high-frequency components of the image. A common kernel used for sharpening is the Laplacian kernel or an unsharp mask.</p>\n<h3>Sharpening with a Kernel</h3>\n<p>The most common sharpening kernel is a 3x3 matrix applied to each pixel and its surrounding pixels. The new value of a pixel is calculated by multiplying each neighboring pixel by a corresponding value in the kernel and summing the results.</p>\n<p>An example of a sharpening kernel is:</p>\n<p>\\[\n\\begin{bmatrix}\n0 & -1 & 0 \\\\\n-1 & 5 & -1 \\\\\n0 & -1 & 0\n\\end{bmatrix}\n\\]</p>\n<p>This kernel emphasizes the central pixel while subtracting the values of the neighboring pixels to increase contrast.</p>\n<h3>Example Calculation</h3>\n<p>Consider a pixel \\( P \\) and its 3x3 neighborhood:</p>\n<p>\\[\n\\begin{bmatrix}\nP_{11} & P_{12} & P_{13} \\\\\nP_{21} & P & P_{23} \\\\\nP_{31} & P_{32} & P_{33}\n\\end{bmatrix}\n\\]</p>\n<p>The new value of pixel \\( P' \\) is calculated as:</p>\n<p>\\[\nP' = (0 \\times P_{11}) + (-1 \\times P_{12}) + (0 \\times P_{13}) + (-1 \\times P_{21}) + (5 \\times P) + (-1 \\times P_{23}) + (0 \\times P_{31}) + (-1 \\times P_{32}) + (0 \\times P_{33})\n\\]</p>\n<p>\\[\nP' = 5P - P_{12} - P_{21} - P_{23} - P_{32}\n\\]</p>\n<h3>Applying the Kernel to the Image</h3>\n<p>The sharpening effect is applied to the entire image by convolving the sharpening 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 sharpened values.</p>\n<p>Sharpening an image enhances its details and edges, making it appear more defined and crisp. The process involves adjusting the pixel values based on their neighbors to increase contrast and highlight fine details.</p>" }