> For the complete documentation index, see [llms.txt](https://docs.kostacloud.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.kostacloud.com/optimization/imaging-optimization.md).

# Imaging Optimization

Imaging optimization is a critical process in the development of optical systems, aimed at achieving the best possible performance - no matter how it is defined.

Optimization involves adjusting various parameters of the lens elements to meet specific criteria, such as minimizing aberrations, achieving a desired field of view, or optimizing image quality across a range of conditions.

#### Key Considerations in Imaging Optimization

1. **Performance Goals:** Define what "optimal" means for the specific application, including resolution, field of view, and light throughput. This is defined through Metrics (also commonly known as operands).
2. **Material Selection:** Different glass materials have unique refractive indices and dispersion properties, impacting the lens design.
3. **Geometrical Parameters:** The arrangement and shape of the lens elements drastically affect the performance and feasibility of the optical system.
4. **Tolerancing :** Understand how manufacturing tolerances can impact the optical performance and adjust the design accordingly. This is crucially important to keep in mind while optimizing a system to provide designs which can be "easily" produced.

#### KostaCLOUD Imaging Metrics&#x20;

In KostaCLOUD, imaging optimization metrics are broken up into several distinct groups, which each have particular assumptions associated with them. You can find details by clicking on the cards below.

<table data-view="cards"><thead><tr><th></th><th></th><th data-hidden data-card-cover data-type="files"></th><th data-hidden data-card-target data-type="content-ref"></th></tr></thead><tbody><tr><td><strong>Paraxial Metrics</strong></td><td>EFL, Invariant, BFD, Magnification, etc.</td><td><a href="https://2927895027-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2FvzB1GHTV5xshI62EhkCW%2Fuploads%2FchoG9eeqq6VTCsxUAfti%2Fimage.png?alt=media&amp;token=10caa1e1-95f3-47f0-bdc5-57610f2de443">image.png</a></td><td><a href="/optimization/imaging-optimization/paraxial-metrics.md">Paraxial Metrics</a></td></tr><tr><td><strong>3rd Order Metrics</strong></td><td>Coma, Spherical, and Petzval coefficients, etc.</td><td><a href="https://2927895027-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2FvzB1GHTV5xshI62EhkCW%2Fuploads%2FJVQhRKAJk5TwTBkElxFW%2Fimage.png?alt=media&amp;token=c247c10d-c755-4963-93b9-400ceb9c8a0b">image.png</a></td><td><a href="/optimization/imaging-optimization/third-order-metrics.md">Third Order Metrics</a></td></tr><tr><td><strong>Real Ray Metrics</strong></td><td>RMS Spot Size, Ray Angle, Axial Color, etc.</td><td><a href="https://2927895027-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2FvzB1GHTV5xshI62EhkCW%2Fuploads%2FmOQ0SwWLssLQ7Y5yDHcb%2Fimage.png?alt=media&amp;token=e3fad680-a9fa-4cb6-9dbe-250aeff3e2bb">image.png</a></td><td><a href="/optimization/imaging-optimization/real-ray-metrics.md">Real Ray Metrics</a></td></tr><tr><td><strong>Wave Metrics</strong></td><td>MTF, Wavefront Error, Encircled Energy, etc.</td><td><a href="https://2927895027-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2FvzB1GHTV5xshI62EhkCW%2Fuploads%2FqwTfV0FX8pvhzCBBTHKQ%2Fimage.png?alt=media&amp;token=82138150-b182-4c42-b4cb-da55c306d053">image.png</a></td><td><a href="/optimization/imaging-optimization/wave-metrics.md">Wave Metrics</a></td></tr><tr><td><strong>System Metrics</strong></td><td>Optical Track, Edge Thickness, etc.</td><td><a href="https://2927895027-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2FvzB1GHTV5xshI62EhkCW%2Fuploads%2FpdMj9sqmMT1ltnl4yRNk%2Fimage.png?alt=media&amp;token=00cfbb9e-5243-47da-b2cb-2fa75de4e0f5">image.png</a></td><td><a href="/optimization/imaging-optimization/system-metrics.md">System Metrics</a></td></tr><tr><td><strong>Tolerancing Metrics</strong></td><td>Compensators, Sensitivities, and solves.</td><td><a href="https://2927895027-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2FvzB1GHTV5xshI62EhkCW%2Fuploads%2FPTNUKlRhpMZRPfmoW6Pg%2Fimage.png?alt=media&amp;token=2f6c3801-3381-4a16-b9a0-c69db254102a">image.png</a></td><td><a href="/optimization/imaging-optimization/tolerancing-metrics.md">Tolerancing Metrics</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 by asking a question.

Perform an HTTP GET request on the following URL with the `ask` and `goal` query parameters:

```
GET https://docs.kostacloud.com/optimization/imaging-optimization.md?ask=<question>&goal=<user_goal>
```

`ask` is the immediate question: it should be specific, self-contained, and written in natural language.
`goal` is what the user is ultimately trying to achieve, the reason they need the answer. Sharing it helps GitBook give you a better, more relevant answer. A goal is most helpful when it describes the outcome the user wants rather than restating the question. For example, with `ask=how do I create an API token`, a goal like `automate deployments from our CI pipeline` lets GitBook tailor the answer to that use case.

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.
