# Image Convolution Kernel Apply standard convolution kernels (blur, sharpen, edge detection, emboss) for image processing and effects > Canonical page: https://elysiatools.com/en/tools/image-convolve - **Category:** Design - **Keywords:** image, convolution, kernel, filter, blur, sharpen, edge, emboss, effects ## Overview Apply mathematical convolution kernels to your images to achieve professional-grade visual effects like blurring, sharpening, edge detection, and embossing. ## Inputs - **Image File** (file): Image to apply convolution kernel - **Kernel Type** (select): Type of convolution kernel to apply - **Intensity** (number): Strength of the convolution effect (0.1-5.0) - **Iterations** (number): Number of times to apply the kernel - **Output Mode** (select): Additional processing on the output ## When to use - When you need to enhance image details or clarify blurry photos using sharpening filters. - When you want to create artistic visual effects like relief, emboss, or motion blur. - When you need to isolate structural elements in an image using edge detection or outline kernels. ## How it works - Upload your image file in a supported format like PNG, JPEG, or WebP. - Select the specific convolution kernel type from the provided list, such as Gaussian Blur or Sobel Gradient. - Adjust the intensity and iteration settings to fine-tune the strength of the effect. - Choose an output mode, such as grayscale or negative, and process the image to download your result. ## Use cases - Restoring clarity to slightly out-of-focus images using the sharpen kernel. - Creating stylized graphic design assets by applying emboss or relief filters. - Extracting structural outlines from technical diagrams or sketches using edge detection. ## Frequently asked questions ### What image formats are supported? The tool supports JPEG, PNG, WebP, GIF, BMP, and TIFF files. ### What is a convolution kernel? It is a small matrix used in image processing to apply effects by calculating new pixel values based on their neighbors. ### Can I apply multiple effects at once? You can use the 'Iterations' setting to apply the same kernel multiple times, or process the image sequentially with different settings. ### What does the intensity setting do? Intensity scales the strength of the kernel effect, allowing you to make filters more subtle or more aggressive. ### Is there a limit to the file size? Yes, the maximum file size for uploads is 10MB. ## Related tools - [Image Blending Effect](https://elysiatools.com/en/tools/image-add-blending): Blend multiple images together using various blend modes for creative compositions and effects - [Bilateral Filter](https://elysiatools.com/en/tools/image-bilateral-filter): Apply bilateral filter for edge-preserving smoothing that reduces noise while maintaining image details - [Image Box Blur](https://elysiatools.com/en/tools/image-box-blur): Apply box blur kernel for simple uniform averaging and fast noise reduction - [Image Color Burn Blend](https://elysiatools.com/en/tools/image-color-burn): Apply color burn blend mode between two images for dramatic darkening and intense color effects - [Image Color Dodge Blend](https://elysiatools.com/en/tools/image-color-dodge): Apply color dodge blend mode between two images for bright, ethereal and glowing effects - [Image Custom Convolution Kernel](https://elysiatools.com/en/tools/image-custom-kernel): Apply custom convolution kernel by defining your own matrix values for personalized image effects and processing - [Image Gaussian Blur](https://elysiatools.com/en/tools/image-gaussian): Apply Gaussian blur kernel for smooth noise reduction and image softening with precise control - [Image Gradient Operation](https://elysiatools.com/en/tools/image-gradient): Apply gradient operations (Sobel, Prewitt, Roberts) for edge detection and image enhancement ## Samples - [Web Image Processing Python Samples](https://elysiatools.com/en/samples/web-image-processing-python): Web Python image processing examples using PIL/Pillow including reading, saving, resizing, and format conversion - [Web Image Processing TypeScript Samples](https://elysiatools.com/en/samples/web-image-processing-typescript): Web TypeScript image processing examples including image read/save, scaling, and format conversion - [Android Image Processing Java Samples](https://elysiatools.com/en/samples/android-image-processing-java): Android Java image processing examples including reading/saving images, scaling, and format conversion - [Android Image Processing Kotlin Samples](https://elysiatools.com/en/samples/android-image-processing-kotlin): Android Kotlin image processing examples including reading/saving images, scaling, and format conversion ## Related content - [Image Edge Detection and Convolution Kernel Lab](https://elysiatools.com/en/hubs/image-edge-detection-and-convolution-kernels): Extract the luminance channel, suppress noise with a recorded radius, compare Sobel, Prewitt, Roberts, and Laplacian operators, tune thresholded edge detection, and reproduce the winning result as an explicit convolution kernel.