Introduction – Examples – Oversharpening and Undersharpening  Examples – Unsharp masking (USM) – Links   Introduction to sharpening Sharpening is an important part of digital image processing. It restores some of the sharpness lost in the lens and image sensor. Every digital image intended for human viewing benefits from sharpening at some point in its workflow— in the camera, the RAW conversion software, and/or the image editor. Sharpening has a bad name with some photographers because it has been overdone in some cameras (mostly low-end compacts and camera phones), resulting in ugly “halo” effects near edges. But it’s entirely beneficial when done properly. The JPEG image output […]

Photographic scientists and engineers stress the fact that no single number satisfactorily describes the ability of a photographic system to reproduce the small-scale attributes of the subject —Leslie Stroebel,John Compton, Ira Current, Richard Zakia Basic Photographic Materials and Processes, Second edition, p. 273 (Micro-image evaluation chapter), Focal Press, 2000 News: Imatest 23.1 contains a new method for calculating the information capacity from slanted-edge patterns, which has been developed and presented in the white paper,  “Measuring Camera Information Capacity with Imatest“. The slanted-edge method is faster and more efficient than the Siemens star method, but not as good for measuring artifacts […]

For more information on how to use INI files in Imatest IT, we recommend the Imatest INI File Reference This document was created by running sfrreg in Imatest 5.2.0. ALPHA on 31-May-2019 14:48:43. For Imatest IT, most of these entries don’t need to be entered. Many don’t affect Imatest IT results: they control interactive figure displays or figure output formats (figures are often not used in Imatest IT). Background Meaning Yellow Yellow background: Important to Imatest IT. Parameter and Description are in boldface. [IT] Cyan Cyan background: Figure settings. (Figures are used infrequently for IT.) [f] Gray Gray background: For […]

These posts describe several topics, including: SFRplus Quadrant analysis (it includes the center region as well), Saturation analysis, which attempts to estimate the severity of saturation in slanted-edge regions (ROIs), and a few other recent additions (November 2012). Saturation is important because saturated light or dark regions will result in artificially high MTF readings. How to select regions at a fixed distance from center to corner (typically in the range of 65-85%)

For more information on how to use INI files in Imatest IT, we recommend the Imatest INI File Reference For Imatest IT, most of these entries don’t need to be entered. Many don’t affect Imatest IT results: they control interactive figure displays or figure output formats (figures are often not used in Imatest IT). Background Meaning Yellow Yellow background: Important to Imatest IT. Parameter and Description are in boldface. [IT] Cyan Cyan background: Figure settings. (Figures are used infrequently for IT.) [f] Gray Gray background: For interactive operation. No effect on IT. Clear Clear background: Results details (units, scaling, etc.) […]

Since SFRplus (and eventually eSFR ISO, which uses almost all the same settings) is included in IT EXE and DLL, users may sometimes need to examine or edit the INI file used to control IT versions. Most of the settings in the [sfrplus] or [esfriso] section are set by one of the three SFRplus or eSFR ISO Settings windows that can be opened when SFRplus is run in Rescharts (or by clicking SFRplus setup in the Imatest main window). A few are set by responses to other windows or user actions. Settings that affect only Rescharts mode (and hence do […]

Imatest SFR allows you to analyze and display several regions of interest (ROIs) in an image. Display options can be selected from three dropdown windows from the SFR settings window. Multi-ROI plots lets you choose the plot type: 1D or 2D; units in Cycles/Pxl, LW/PH (Line Widths per Picture Height), or LP/PH (Line Pairs per Picture Height).The 1D summary plots, which display results as a function of the distance from the image center, may be difficult to read for lenses that are poorly centered and hence have asymmetrical response. In most cases 2D summary plots are far more readable. 1D […]

* indicates that this field will be described in more detail at the bottom of this document (Much of the text at the bottom will be common to several ini file reference pages). For more information on how to use INI files in Imatest IT, we recommend the Imatest INI File Reference For Imatest IT, most of these entries don’t need to be entered. Many don’t affect Imatest IT results: they control interactive figure displays or figure output formats (figures are often not used in Imatest IT). Background Meaning Yellow Yellow background: Important to Imatest IT. Parameter and Description are […]

Imatest Rescharts slanted-edge modules perform highly automated measurements of several key image quality factors using specially-designed test charts. The user never has to manually select Regions of Interest (ROIs). This page covers results that are (mostly) not derived from the slanted-edges themselves, including Noise (best in eSFR ISO) Distortion  (differing detail in different modules; best with SFRplus and eSFR ISO. Described in detail here. Tonal response*  (no noise statistics for SFRplus) Color accuracy*  when used with an SFRplus, eSFR ISO, or SFRreg center charts that contain a color pattern Vanishing resolution, aliasing, and Moiré from Wedge patterns in eSFR ISO ISO sensitivity*  (Saturation-based and […]

Imatest Rescharts slanted-edge modules perform highly automated measurements of several key image quality factors using specially-designed test charts. The user does not need to manually select Regions of Interest (ROIs). This page covers results that are derived from the slanted-edges (i.e., not from grayscale, color, or wedge patterns). It also covers text output (CSV and JSON) files. Sharpness, expressed as Spatial Frequency Response (SFR), also known as the Modulation Transfer Function (MTF), can be displayed in several ways for individual edges or from the entire pattern, Lateral Chromatic Aberration Information capacity Other results, not derived from slanted-edges are covered in Part […]

Introduction – Using raw files – Bayer raw and RCCC files – LibRaw demosaicing (for commercial raw files)  Differences with in-camera JPEGs – Monochrome images – Bayer frequency units  DNG files – Rawview utility – Generalized Read Raw (for binary raw files)  Decompanding – Estimating image width & height – Creating Synthetic raw images    The unprocessed digital output of an image sensor is called RAW image data. In this document, we sometimes refer to raw files from commercial cameras or development systems as Camera raw to distinguish them from Bayer raw files, which are standard monochrome image files that contain undemosaiced […]

Automatic region detection in SFRplus and eSFR ISO tolerates moderate amounts of optical distortion (pincushion or barrel), but it has definite limits. In this page we describe special versions of SFRplus and eSFR ISO charts that can work (i.e., can be detected automatically) with highly barrel-distorted (“fisheye“) lenses, with fields of view up to around 160 degrees— which are used in a number of applications, particularly for automotive rear-view and sports cameras. Cameras with fields of view over 160 degrees— even approaching 360 degrees— can be tested with the SFRreg module, which uses multiple individual “registration mark” charts facing the […]

Although sharpness is an important image quality factor, a sharper lens is not always better. A lens can be too sharp for a sensor, resulting in disturbing visual artifacts. These artifacts, which include “stair-stepping” and moiré patterns (low frequency patterns that can be strongly colored), can appear because digital cameras— and all digitally sampled systems— have a maximum spatial frequency, called the Nyquist frequency, beyond which scene information cannot be correctly reproduced. Any information above the Nyquist frequency that reaches the sensor will be “aliased” to a lower spatial frequency, which can result in the artifacts described below. Sampling – Nyquist […]

Imatest can correct for nonuniform illumination and lens response (vignetting) in Imatest modules that analyze grayscale and/or color charts, including Color/Tone Setup (formerly Color/Tone Interactive), Color/Tone Auto (formerly Color/Tone Auto), Colorcheck, and Stepchart. Nonuniformity correction involves reading and specifying a second image, taken from a flat-field target (plain gray or white) under identical conditions to the test image.  Note that the correction described in this page is not the same as nonuniformity correction for slanted-edge MTF measurements. Not also that Color/Tone Interactive (a highly interactive module) and Color/Tone Auto (a batch-capable fixed version of Color/Tone Interactive) are recommended for new […]

News: Imatest 23.1 (March 2023) (available in the Imatest Pilot program). New methods for calculating camera information capacity, Noise Power Spectrum (NPS), Noise Equivalent Quanta (NEQ), and Ideal observer SNR (SNRi) from slanted-edge patterns are now available. Shannon information capacity can also be calculated from images of the Siemens star, described in the 2020 white paper, Camera information capacity: a key performance indicator for Machine Vision and Artificial Intelligence systems. Siemens Star measurements are the recommended method for calculating information capacity when artifacts from image processing (demosaicing, data compression, etc.) are of importance. Slanted-edge measurements are faster, more convenient, and better for […]

The Imatest Classic main window is the heart of Imatest Master: it’s where you open the analysis and utility modules. It’s complex but powerful. It consists of several well-populated dropdown menus, two columns of buttons (fixed and interactive analysis modules), and a tabbed area where you can select Utility, Data, and Help tabs. Imatest main window The contents of the three tabs on the right, Utility, Data, and Help , are shown in the right three columns of the table below, which has a similar structure as the Imatest main window. Map of Imatest main window, with links to instructions […]

Analysis of log frequency-varying charts Introduction Log frequency, which uses the Rescharts interface, measures the contrast of narrow bar or sine charts that increase logarithmically in spatial frequency. It also measures color Moiré (Imatest Master only). When the image pattern is sinusoidal (rather than a bar chart), contrast is equivalent to SFR or MTF. This method is more direct than the slanted-edge method, but less accurate and more susceptible to noise. A chart can be created by Test Charts and printed on a high quality inkjet printer. Log Frequency image (complete and cropped) The image above used to illustrate the […]

Analysis of Log Frequency-Contrast charts New in Imatest 4.0  Automatic region is available with the revised version of the chart, which includes registration marks in the corners. Sharpness and Texture Analysis using Log F‑Contrast from Imaging-Resource compares the the effects of sharpening and noise reduction in several cameras using images downloaded from Imaging-Resource.com. Introduction Log F-Contrast (short for Log Frequency-Contrast; not in Imatest Studio) measures the effects of signal processing— noise reduction and sharpening— on imaging system performance using a chart that varies in spatial frequency on the horizontal axis (log frequency increases with x) and in modulation ((max-min)/(max+min) signal, […]

  The basic premise of this work is that Information capacity is a superior metric for predicting the performance of imaging systems. It is better than sharpness or noise, which it incorporates, and it can be used  to derive metrics for measuring object and edge detection performance and for designing electronic filters that optimize system performance.   Related pages Image Information Metrics: Information Capacity and more contains key links to documentation, white papers, news, and more on image information metrics. The Electronic Imaging 2024 paper, Image information metrics from slanted edges, contains the best exposition of the image information metrics. The […]

  The basic premise of this work is that Information capacity and related metrics for measuring edge and object detection performance are superior to familiar metrics (sharpness and noise) for predicting and optimizing the performance of imaging systems.   Related pages Image Information Metrics: Information Capacity and more contains key links to documentation, white papers, news, and more on image information metrics. The revised (August 2024) paper from Electronic Imaging 2024, Image information metrics from slanted edges, contains the most complete and up-to-date exposition of the image information metrics. The material is also covered in various levels of detail in the […]