The “Health Check Report” of Vision Modules

Understanding MTF and SFR Testing in One Article

Inside the darkroom of a testing laboratory, a smartphone camera faces a carefully illuminated slanted-edge test chart. An engineer clicks the analysis button, and a smooth curve instantly appears on the screen—descending from high to low—accurately predicting how clearly this camera will capture the world.

When evaluating a camera, a lens, or even an entire imaging system, sharpness is often the most intuitive requirement. But how can this subjective visual perception be transformed into objective, repeatable, and scientific data?

This is precisely the role of Modulation Transfer Function (MTF) and Spatial Frequency Response (SFR). They are the universal language of optics and imaging, and the gold standard for quantitatively evaluating image sharpness and detail reproduction in vision modules.

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01 Core Concepts: From Blur to Quantification

What Is MTF?

Simply put, MTF measures the “fidelity” of an imaging system. It describes how well the system can reproduce the contrast of black-and-white line patterns at different levels of fineness (spatial frequencies).

The basic modulation formula is:

M = (Imax − Imin) / (Imax + Imin)

The MTF value is defined as the ratio between the image modulation and the original object modulation.
An ideal MTF value of 1 represents perfect reproduction.

In testing, spatial frequency is measured in line pairs per millimeter (lp/mm) and can be understood as the fineness of image details. Higher spatial frequency corresponds to denser lines and finer details.

A general rule applies: as spatial frequency increases, MTF typically decreases, because an imaging system’s ability to reproduce extremely fine details is always limited.

For example:

• At 20 lp/mm, an MTF value above 80% indicates excellent image quality

• If the MTF falls below 30%, image quality is already poor even at standard print sizes

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What Is SFR?

SFR is often described as the “digital cousin” of MTF. It is specifically designed to evaluate complete digital imaging systems, including the lens, image sensor, and image signal processing (ISP).

The goal of SFR is to convert human-perceived “sharpness” into measurable physical parameters.

SFR works by analyzing the imaging system’s response to a slanted sharp edge, from which the system’s overall frequency response can be derived.

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02 Physical Principles: How Details Are Lost

Why do images become blurred? From a physical perspective, image degradation in digital imaging systems results from multiple factors, which can be grouped into three main categories:

Hardware Limitations

• Lenses inherently suffer from residual geometric aberrations and diffraction

• Sensor pixel aperture and electrical crosstalk between pixels lead to detail loss

Software Algorithm Effects

To produce more visually pleasing images, algorithms such as noise reduction, beautification, and background blur actively modify pixel data. While reducing noise, they often remove genuine fine details.

Environmental Influences

Camera shake, atmospheric turbulence, and stray light under strong illumination can all directly degrade image sharpness.

MTF and SFR quantify the combined impact of these factors by treating the imaging process as a signal-processing system:

• Input: original scene information

• Output: final image

The system modifies the contrast (amplitude) of different frequency components.
The MTF/SFR curve visually shows how much contrast is preserved at each spatial frequency.

A key principle is that the overall system MTF curve is the product of the MTF curves of individual components (lens, sensor, optical low-pass filter, etc.).
Therefore, analyzing the MTF curve allows engineers to pinpoint bottlenecks and optimize system design with precision.

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03 Testing in Practice: How Is Sharpness “Scored”?

Standardized testing methods are the foundation of result comparability.
The internationally recognized standard for SFR testing is ISO 12233, first released in 2000 and continuously updated since.

Core Testing Principle

The standard uses a slanted edge (typically tilted by ~5°) as the test target.
This slight tilt is crucial—it creates multiple phase samples, enabling oversampling.

This approach compensates for uncertainties caused by the discrete pixel array of image sensors, allowing accurate calculation of the Edge Spread Function (ESF).

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Standard Test Workflow

1. Environment Setup
   Testing is performed in a darkroom using standardized illumination (e.g., 6500K color temperature, 800 lux), ensuring uniform lighting of the test chart.

2. Image Capture
   The device under test (DUT) is securely mounted, precisely focused, and used to capture images of the test chart.

3. Software Analysis
   Professional software such as Imatest Master or RIQA automatically detects the slanted-edge region and performs a sequence of calculations:

   • Fit a supersampled Edge Spread Function (ESF)

   • Compute the first derivative of ESF to obtain the Line Spread Function (LSF)

   • Apply Fourier transform to the LSF and take the magnitude, resulting in the final SFR (MTF) curve

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Table 1: Key SFR Metrics Explained

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04 Practical Setup: Building Your Test System

Depending on testing objectives, accuracy requirements, and budget, system configurations can be categorized into several levels.

Basic R&D and Validation Setup

• ISO-compliant SFR test charts (slanted-edge, SFRplus, etc.)

• Standard light sources with adjustable color temperature and illuminance

• Image quality analysis software such as Imatest Master or RIQA

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Professional Optical Laboratory Setup

• Addition of a collimator, which simulates infinity-focus conditions within a limited space

• Essential for long-focus lenses and infinity-focus performance evaluation

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Advanced Automated Test Systems

High-end labs deploy fully automated systems integrating:

• Motorized collimators

• High-precision six-axis motion platforms

• Automated image capture, multi-angle alignment, data analysis, and report generation

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Table 2: Typical Test Equipment Configurations

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Production-Line Quality Control

In mass production, priorities shift to speed, consistency, and automation. Integrated inline test equipment can complete image capture, analysis, and pass/fail judgment within seconds, automatically uploading results to MES systems for real-time quality monitoring and traceability.

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When engineers debate optimization strategies while studying overlapping MTF curves, they are not merely discussing abstract percentages.
Each improvement in the curve means:

• Autonomous vehicles detect road signs earlier

• Medical endoscopes reveal finer tissue details

• Smartphones capture fleeting moments with greater clarity

Behind every MTF curve is a clearer world.