# Image Noise Reduction

Reduce image noise using multiple algorithms including median, gaussian, and bilateral filtering

> Canonical page: https://elysiatools.com/en/tools/image-noise-reduction

- **Category:** Design

- **Keywords:** image, noise, reduction, denoise, median, gaussian, bilateral, filter, smooth, clean

## Overview

Improve your image quality by removing unwanted grain and artifacts with our professional-grade Image Noise Reduction tool, featuring advanced algorithms like median, Gaussian, and bilateral filtering.

## Inputs

- **Image File** (file): Upload any image file (JPEG, PNG, WebP, GIF, BMP, TIFF) to reduce noise and improve image quality
- **Noise Reduction Algorithm** (select): Choose the denoising algorithm. Median works well for salt & pepper noise, Gaussian for random noise
- **Noise Reduction Strength** (range): Strength of noise reduction. Higher values remove more noise but may blur details
- **Filter Kernel Size** (range): Size of the filter kernel (must be odd). Larger kernels remove more noise but may blur details
- **Filter Iterations** (number): Number of denoising passes. More iterations = stronger noise reduction
- **Preserve Edges** (checkbox): Maintain edge detail while removing noise. Recommended for photos with important details
- **Output Format** (select): Choose output format or keep original. PNG recommended for maximum quality
- **Quality (1-100)** (number): Output quality for lossy formats (JPEG, WebP). Higher = better quality but larger file size

## When to use

- Cleaning up grainy photos taken in low-light conditions.
- Removing digital artifacts from compressed or low-resolution images.
- Smoothing out textures while preserving essential edge details for professional prints.

## How it works

- Upload your image file (JPEG, PNG, WebP, etc.) to the tool.
- Select the appropriate denoising algorithm based on the type of noise present.
- Adjust the strength, kernel size, and iteration settings to fine-tune the output.
- Apply edge preservation if needed and download your cleaned image in your preferred format.

## Use cases

- Restoring vintage scanned photographs with heavy film grain.
- Enhancing smartphone photos taken in dark environments.
- Preparing web graphics by removing compression artifacts from low-quality source files.

## Frequently asked questions

### Which algorithm should I choose?

Use Median for salt & pepper noise, Gaussian for random grain, and Bilateral for preserving sharp edges while smoothing surfaces.

### Does increasing the strength blur my image?

Yes, higher strength values remove more noise but can lead to a loss of fine detail. We recommend using the 'Preserve Edges' option to mitigate this.

### What is the maximum file size I can upload?

You can upload image files up to 20MB in size.

### Can I change the output format?

Yes, you can convert your image to JPEG, PNG, or WebP, or simply keep the original format.

### What does the kernel size do?

The kernel size determines the area of pixels analyzed for noise. Larger kernels cover more area, resulting in stronger smoothing.

## Related tools

- [Image Trim Whitespace](https://elysiatools.com/en/tools/image-trim-whitespace): Remove whitespace and empty margins from images by detecting and trimming blank areas
- [Image Box Blur](https://elysiatools.com/en/tools/image-box-blur): Apply box blur kernel for simple uniform averaging and fast noise reduction
- [Remove Image Background](https://elysiatools.com/en/tools/image-background-remover): Remove image backgrounds and export a transparent PNG
- [Median Pass Filter](https://elysiatools.com/en/tools/image-median-pass): Apply median pass filter to remove noise while preserving edges with multiple algorithms
- [Image Morphological Opening](https://elysiatools.com/en/tools/image-open): Apply morphological opening operation (erosion followed by dilation) to remove small noise and separate connected objects
- [Image Color Extractor](https://elysiatools.com/en/tools/image-color-extractor): Extract dominant colors from images using various algorithms including k-means clustering and histogram analysis
- [Image Adaptive Threshold](https://elysiatools.com/en/tools/image-adaptive-threshold): Apply adaptive threshold operation that calculates threshold for small regions, suitable for varying lighting conditions
- [Image Blending Effect](https://elysiatools.com/en/tools/image-add-blending): Blend multiple images together using various blend modes for creative compositions and effects

## 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 Padding, Noise Reduction, and Final Sharpening](https://elysiatools.com/en/hubs/image-padding-noise-and-sharpening): Prepare an image for a fixed aspect ratio by preserving the source, adding controlled borders, reducing distracting noise lightly, and sharpening only at the intended delivery size.