ASTM E2825-21

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Designation: E2825 − 19 E2825 − 21
Standard Guide for
Forensic Digital Image Processing
This standard is issued under the fixed designation E2825; the number immediately following the designation indicates the year of
original adoption or, in the case of revision, the year of last revision. A number in parentheses indicates the year of last reapproval. A
superscript epsilon (´) indicates an editorial change since the last revision or reapproval.
1. Scope
1.1 This guide provides digital image processing guidelines to ensure the production of quality forensic imagery for use as
evidence in a court of law.
1.2 This guide briefly describes advantages, disadvantages, and potential limitations of each major process.
1.3 This standard cannot replace knowledge, skills, or abilities acquired through education, training, and experience, and is to
be used in conjunction with professional judgment by individuals with such discipline-specific knowledge, skills, and abilities.
1.4 This standard does not purport to address all of the safety concerns, if any, associated with its use. It is the responsibility
of the user of this standard to establish appropriate safety, health, and environmental practices and determine the applicability of
regulatory limitations prior to use.
1.5 This international standard was developed in accordance with internationally recognized principles on standardization
established in the Decision on Principles for the Development of International Standards, Guides and Recommendations issued
by the World Trade Organization Technical Barriers to Trade (TBT) Committee.
2. Referenced Documents
2.1 ASTM Standards:
E1732 Terminology Relating to Forensic Science
E2916 Terminology for Digital and Multimedia Evidence Examination
This guide is under the jurisdiction of ASTM Committee E30 on Forensic Sciences and is the direct responsibility of Subcommittee E30.12 on Digital and Multimedia
Evidence.
Current edition approved June 1, 2019Oct. 1, 2021. Published June 2019October 2021. Originally approved in 2012. Last previous edition approved in 20182019 as E2825
– 18.19. DOI: 10.1520/E2825-19.10.1520/E2825-21.
For referenced ASTM standards, visit the ASTM website, www.astm.org, or contact ASTM Customer Service at service@astm.org. For Annual Book of ASTM Standards
volume information, refer to the standard’s Document Summary page on the ASTM website.
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2.2 ISO/IEC Standard:
ISO/IEC 10918-1:1994 Information technology—Digital compression and coding of continuous-tone still images: Requirements
and guidelines (JPEG) (also published as CCITT Recommendation T.81 (1992))
3. Terminology
3.1 Definitions—For definitions of terms relating to this standard, refer to Terminologies E1732 and E2916.
4. Summary of Practice
4.1 The original image shall be preserved. Any image processing should be applied only to a working copy of the image.
4.2 Any changes made through image processing shall meet the following criteria:
4.2.1 Processing steps are documented in a manner sufficient to permit a comparably trained person to understand the steps taken,
the techniques used, and extract comparable information from the image; and
4.2.2 The end result is presented as a processed or working copy of the image.
4.3 Avoid the introduction of artifacts that add misleading information to the image or the loss of image detail that could lead to
an erroneous interpretation.
5. Significance and Use
5.1 Processed images are used for many purposes by the forensic science community. They can yield information not readily
apparent in the original image, which can assist an expert in drawing a conclusion that might not otherwise be reached.
5.2 This guide addresses image processing and related legal considerations in the following three categories:
5.2.1 Image enhancement,
5.2.2 Image restoration, and
5.2.3 Image compression.
6. Image Enhancement
6.1 Image enhancement is any process intended to improve the visual appearance of an image.
6.1.1 Use brightness adjustment when the image is too bright or too dark. If the image is made too bright, there is a risk of loss
of detail in light areas. If the image is made too dark, there is a risk of loss of detail in the dark areas.
6.1.2 Use color processing to modify the color characteristics of objects within an image. This includes color space
transformations, pseudocoloring, and hue and saturation adjustments.
6.1.2.1 Application of these techniques can compromise the color fidelity of the image.
6.1.3 Use contrast adjustment when the image lacks sufficient contrast. If the image contrast is increased too much, there is a risk
of loss of detail in both light and dark areas.
6.1.4 Use cropping to remove that portion of the image that is outside the area of interest.
6.1.5 Use dodging and burning to adjust brightness brightness and contrast adjustments in localized areas.
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6.1.6 Use linear filtering techniques (see Fig. 1) to increase the contrast of small detail in an image. These include sharpening, blur
removal, edge enhancement, and deconvolution. If a low degree of enhancement is used, the image will remain an accurate
representation of the scene. If a high degree of enhancement is used, the image may no longer be an accurate representation of
the overall scene, though it still may be useful as an adjunct for interpretation of small details.
6.1.6.1 A high degree of enhancement can also increase the visibility of existing noise and artifacts; examples of noise include
film grain, snow appearing on a television screen, or random color dots.
6.1.7 Use nonlinear contrast adjustments to adjust the contrast in selected brightness ranges within the image. These include
gamma correction, grayscale transformation, and the use of curves or look-up tables, or both.
6.1.7.1 A nonlinear contrast adjustment can be used to bring out details in the shadow areas of an image without affecting the
highlight areas.
6.1.7.2 A severe adjustment can cause loss of detail, color reversal, or the introduction of artifacts, or a combination thereof. (See
Fig. 2.)
6.1.8 Use pattern noise reduction filters to identify repeating patterns in an image and selectively remove them. This type of filter
can be used to remove patterns such as fabric weaves, window screens, security patterns, and halftone dots.
6.1.8.1 Overuse of this technique will remove material image detail.
6.1.9 Use random noise reduction techniques to reduce the contrast of small detail in the image to suppress random noise. These
include such filters as low-pass filtering, Gaussian blurring, median filtering, and speckle removing.
6.1.9.1 Overuse of this technique will remove material image detail.
6.1.10 Use warping to change the spatial relationships among the objects in an image. It is analogous to printing a photograph on
a rubber sheet, then stretching the sheet in different directions, and then tacking it down. Warping can be used, for example, to
remove perspective from an image or to "unroll" a poster that was wrapped around a pole.
FIG. 1 This Example Illustrates the Effects of Linear Filtering—Left: Original Image, Middle: Blurred Image, and Right: Sharpened Image
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FIG. 2 This Example Shows Nonlinear Contrast Adjustments—Left: Original Image, Middle: Enhancement of Shadow and Highlight Ar-
eas at the Expense of Midrange Tones, and Right: Enhancement of Midrange Tones at the Expense of Shadow and Highlight Areas
6.1.10.1 Used improperly, warping can distort the natural appearance of the objects in a scene.
7. Image Restoration
7.1 Image restoration is any process applied to an image that has been degraded by a known cause (for example, defocus or motion
blur) to remove the effects of that degradation partially or totally.
7.2 Information that has been totally lost in the image during the original imaging process cannot be replaced through restoration.
However, partial restoration can be successful even when total restoration is impossible.
7.3 Restoration Techniques:
7.3.1 Use blur removal to remove partially or completely an image blur imposed by a known cause.
7.3.1.1 Blur removal differs from the image enhancement filtering processes because the blur removal filter is designed specifically
for the process that blurred the particular image under examination. Examples include defocus and motion blur, since these
phenomena can be described mathematically. Thus, a specific filter can be designed to compensate for each blur. The degree to
which a blur can be successfully removed is limited by noise in the image, the accuracy with which the actual blurring process
can be described mathematically, and the fact that information that has been totally lost cannot be replaced. Often partial blur
removal can be successful even when total blur removal is impossible.
7.3.2 Use color balancing to render the colors in the scene more accurately. Color balancing is the extension of grayscale
linearization to a color image and the adjustment of the color components of an image. For example, a color test target having
known colors can be placed in the scene before recording the image. Then a grayscale transformation (nonlinear contrast stretch)
can be designed for each color channel (red, green, and blue) to place the different colors on the test target in their proper
relationship. This should reproduce the other objects in the scene in their proper relatio
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