# Auto Sharpen

### Model Overview

Using advanced image analysis, our **Auto Sharpen** model analyzes the image using AI for technical quality and content of an image. It distinguishes between true detail and noise, then applies highly accurate sharpening based on the content and the technical quality of the image. The result is a crisp, natural-looking image that preserves fine detail, avoids artifacts, and naturally sharpens the image regardless of subject or lighting conditions. Works best on production-grade media and high resolution images.

### Key Capabilities

* Automatically detects optimal sharpening based on input image quality
* Works across all subject types — portraits, wildlife, landscapes, architecture, and more
* No parameter configuration required; ideal as a default or starting point
* Produces the same result as calling Portrait or Wildlife with no user-specified parameters

### 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>

* `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**.
* `sharpen_strength` — (optional) decimal between 0 and 1, default `null`
  * Amount of sharpening to apply to the image. **Recommended: omit for automatic parameter selection**, for manual ranges \[0.1 - 0.5] for best results.
* `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="https://4007072434-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2FJkSwaYgAsTQke14gpXAF%2Fuploads%2FDusInobqDh4fgdcuCp4U%2F9335787003_slider.jpg?alt=media&#x26;token=f64d678f-461b-4b14-906c-f76fe7dcad60">9335787003_slider.jpg</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="https://4007072434-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2FJkSwaYgAsTQke14gpXAF%2Fuploads%2Fh2jmN2qLR9nGlhOpTgqq%2Fpexels-mnannapaneni_slider.jpg?alt=media&#x26;token=1f5947f9-7f15-4ef5-99a6-eb66e5ea104b">pexels-mnannapaneni_slider.jpg</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="https://4007072434-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2FJkSwaYgAsTQke14gpXAF%2Fuploads%2FZ0WsbICZup7sc8cRc3nT%2Fparker-johnson_slider.jpg?alt=media&#x26;token=db2d8d5a-981a-4844-bac6-bf10af56bdf4">parker-johnson_slider.jpg</a></td></tr></tbody></table>