> For the complete documentation index, see [llms.txt](https://developer.topazlabs.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://developer.topazlabs.com/image-models/removal-and-cleanup/sharpen/noise-aware-sharpen.md).

# Noise-Aware Sharpen

{% hint style="info" %}
This model was formerly known as "Auto Sharpen".
{% endhint %}

### Model Overview

**Noise-Aware Sharpen** is an intelligent automatic sharpening model that uses AI-driven image analysis to evaluate both image quality and scene content before applying sharpening. Unlike traditional sharpening tools that enhance detail and noise equally, the model automatically separates true image detail from digital noise and compression artifacts before intelligently restoring clarity. The result is sharper textures, facial features, fur, and fine edges while preserving the natural character of the original image.

By avoiding aggressive edge enhancement, Noise-Aware Sharpen reduces halos, oversharpening, and harsh digital artifacts commonly introduced by conventional sharpening filters. This model is especially effective for low-light, high ISO, wildlife, sports, nighttime, or grain-heavy photography where visible grain or noise is already present or intentionally part of the atmosphere of the shot. It performs best on high-resolution and production-quality imagery where preserving realism and fine detail is critical.

### Key Capabilities

* One-click intelligent sharpening with no manual tuning required
* Separates image detail from noise before sharpening
* Preserves natural texture and realistic image character
* Reduces halos, oversharpening, and sharpening artifacts

### Model Pricing

<table data-header-hidden data-full-width="false"><thead><tr><th width="119.84765625">Text</th><th width="60.390625" data-type="number">Number</th><th width="59.5390625" data-type="number"></th><th width="60.0859375" data-type="number"></th><th width="60.265625" data-type="number"></th><th width="59.94140625" data-type="number"></th><th width="59.5390625" data-type="number"></th><th width="60.34375" data-type="number"></th><th width="59.9296875" data-type="number"></th><th width="59.828125" data-type="number"></th><th width="59.5078125" data-type="number"></th></tr></thead><tbody><tr><td><code>Output MP</code></td><td>1</td><td>4</td><td>8</td><td>16</td><td>24</td><td>32</td><td>40</td><td>50</td><td>64</td><td>100</td></tr><tr><td><code>Credits</code></td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>2</td><td>2</td><td>3</td><td>3</td><td>5</td></tr></tbody></table>

### Getting Started

**Endpoint**

* <https://api.topazlabs.com/image/v1/sharpen/async>
  * `model` — `"Auto Sharpen"`

{% hint style="info" %}
In order for `"Auto Sharpen"` to be called, all parameters below must be left as null. This is recommended for batch image processing with various image subject types.
{% endhint %}

<details>

<summary><strong>Model-Specific Parameters</strong></summary>

* `preset` — `'normal'`, `'strong'`
  * Controls the strength of the sharpening `'normal'` for highest fidelity, `'strong'` for extra sharpening to be applied and the most visible results. Defaults to `'normal'`. **Recommended: omit for automatic parameter selection.**
* `denoise_strength` — (optional) decimal between 0 and 1, default `null`
  * Amount of noise to remove from the image (for noisy input images, for non-noisy omit). **Recommended: omit for automatic parameter selection**.
* `opacity` — (optional) decimal between 0 and 1, default `null`
  * Controls blending with the original, **Recommended: omit for automatic parameter selection**, decrease opacity on very strong sharpening levels (outside the recommended range).

</details>

**Resolution Limits**

* `input` — 512 MP
* `output` — 512 MP

### Examples

<table data-card-size="large" data-view="cards"><thead><tr><th align="center"></th><th data-hidden data-card-target data-type="content-ref"></th><th data-hidden data-card-cover data-type="image">Cover image</th></tr></thead><tbody><tr><td align="center">Click to view</td><td><a href="https://compare.topazlabs.com/share/image?src1=https://downloads.topazlabs.com/web-assets/9335787003.jpg&#x26;src1label=Before&#x26;src2=https://downloads.topazlabs.com/web-assets/53063746-d55b-4b08-accd-e5d405837452.png&#x26;src2label=After">https://compare.topazlabs.com/share/image?src1=https://downloads.topazlabs.com/web-assets/9335787003.jpg&#x26;src1label=Before&#x26;src2=https://downloads.topazlabs.com/web-assets/53063746-d55b-4b08-accd-e5d405837452.png&#x26;src2label=After</a></td><td><a href="/files/DdYTg4zEVoOHM8VYEC5P">/files/DdYTg4zEVoOHM8VYEC5P</a></td></tr><tr><td align="center">Click to view</td><td><a href="https://compare.topazlabs.com/share/image?src1=https://downloads.topazlabs.com/web-assets/pexels-mnannapaneni-29388749.jpg&#x26;src1label=Before&#x26;src2=https://downloads.topazlabs.com/web-assets/82d2c77f-e8a4-4a91-9762-c02b1becb517.png&#x26;src2label=After">https://compare.topazlabs.com/share/image?src1=https://downloads.topazlabs.com/web-assets/pexels-mnannapaneni-29388749.jpg&#x26;src1label=Before&#x26;src2=https://downloads.topazlabs.com/web-assets/82d2c77f-e8a4-4a91-9762-c02b1becb517.png&#x26;src2label=After</a></td><td><a href="/files/Tifr24mhUi6OoXcW0l8S">/files/Tifr24mhUi6OoXcW0l8S</a></td></tr><tr><td align="center">Click to view</td><td><a href="https://compare.topazlabs.com/share/image?src1=https://downloads.topazlabs.com/web-assets/parker-johnson-MOPTew6XlTA-unsplash.jpg&#x26;src1label=Before&#x26;src2=https://downloads.topazlabs.com/web-assets/eed144e0-da8b-4d8d-aa82-998f071e8d11.png&#x26;src2label=After">https://compare.topazlabs.com/share/image?src1=https://downloads.topazlabs.com/web-assets/parker-johnson-MOPTew6XlTA-unsplash.jpg&#x26;src1label=Before&#x26;src2=https://downloads.topazlabs.com/web-assets/eed144e0-da8b-4d8d-aa82-998f071e8d11.png&#x26;src2label=After</a></td><td><a href="/files/w5YHQKpoVlZy5qFoHjOJ">/files/w5YHQKpoVlZy5qFoHjOJ</a></td></tr></tbody></table>


---

# Agent Instructions
This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com.

## Querying This Documentation
If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter:

```
GET https://developer.topazlabs.com/image-models/removal-and-cleanup/sharpen/noise-aware-sharpen.md?ask=<question>
```

The question should be specific, self-contained, and written in natural language.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.