2024-09-29 Feelings at this time

  • Resolution and resolving power are two different things.
    • In some cases, what is called “resolution” in the world is actually “resolving power.
    • Coarse thinking and fact recognition Coarse thought output is “low resolution” and coarse fact recognition and thought process is “low resolution.”
  • On the other hand, there is no real benefit in distinguishing between “resolution” and “resolving power” in many discussions in the world.
    • Just like when explaining how to use a web app to someone with low IT literacy, you don’t distinguish which part is working on the front end and which part is working on the server side.
    • The use of the terms resolution and resolving power for human cognition is a metaphor, and it is important whether it is useful or not, not whether it is correct or not, since [All metaphors are wrong.
    • It is useful to think about the human thought process.

2023-11-17 Roughly organized at this point in time

ochyai A word I popularized and incorrectly conveyed: “resolution.”

For those who do not understand the difference between resolution and resolution, it is correct to replace it with “resolution”.

ochyai “Resolution” has probably been in use since about 2017 (per weeklyochiai). I’m a display, camera, lens, and holographic shop, so I often use “resolution, resolving power, and resolution”, but most business uncles are not display, camera, lens, and holographic shops, so I want to start a resolution police soon. (ref Hologram Police)

nishio I’m not sure what the word “resolution” means other than “resolution”, which is guilty or innocent in this usage? Cognitive Resolution ochyai This seems to be a resolving power nishio I understand that “resolution” is the high definition of the output, such as a display, and “resolving power” is the high definition of the input, such as a camera, and since human perception is not of the display but of the camera, ” Is my understanding correct that “resolution” is correct? ochyai Since it is used in the sense of “finely cognizable”, I thought it would be possible to prepare a cognitive chart or something similar. In that case, as you say, resolving power seems to be correct.

4:3 Resolution and Focus Chart|Murakami Color Technology Institute, Inc. image

ResOchyAI: I’ll teach you resolution

  • image Resolution and resolving power are often confused, but they refer to different concepts. Here we explain the difference between them.

  • resolution (e.g. display, dpi) (Resolution)

  • Definition: Resolution is a measure of how fine an image or display is. It is typically measured in terms of the number of pixels in an image or the pixel density (number of pixels/area) of a display. For example, a resolution of 1920x1080 means an image with 1920 horizontal pixels and 1080 vertical pixels.

  • Uses: Often used in fields related to image display and printing, such as digital cameras, monitors, televisions, and printers.

  • Importance: High-resolution devices and images can show more detail, as it is crucial to the sharpness and detail of an image.

[Resolving Power

  • Definition: Resolution refers to the ability to distinguish two close points or lines separately. It is a measure of the performance of an optical system (camera lens, microscope, etc.) and is generally expressed in terms of spatial frequency (number of lines or points per unit distance).
  • Uses: It is primarily used to evaluate optical systems. For example, the resolving power of a microscope or telescope indicates how well they can distinguish fine details.
  • Importance: Reflects the performance of the optical system and how well it captures the finest details of the subject.

Key Points of Difference

  • Target: Resolution is an attribute of the image or display itself, while resolving power indicates the performance of the optical system.
  • Measurements: Resolution is measured in pixels or dots, while resolving power is measured in spatial frequency.
  • Applications: Resolution is relevant to image processing and display technology, while resolving power is relevant to optical system design and performance evaluation.

Understanding these differences is important in the use of appropriate technical terminology, device selection, and performance evaluation.

ochyai Please inspect the “Resolution Rehabilitation Facility” for the “Resolution Police”. image image

ochyai: Resolution Collapse: OTF/MTF/PSF/LSF understood by business comedians is required. Here it is!

gpt.icon

OTF (Optical Transfer Function)

  • Definition: OTF is the response characteristics of an optical system in the frequency domain. It shows how the system responds to different spatial frequencies (i.e., patterns of different fineness).
  • Calculation: OTF is defined as the Fourier transform of the PSF (Point Spread Function). It transforms how an optical system blurs a point source (PSF) into how it transmits different frequency components (OTF). MTF (Modulation Transfer Function)
  • Definition: MTF is defined as the magnitude (absolute value) of the OTF. It indicates how an optical system transfers contrast in an image.
  • Applications: Mainly used to evaluate the performance of optical instruments such as camera lenses and microscopes. High MTF values mean high image contrast and sharp details. PSF (Point Spread Function)
  • Definition: The PSF is a function of how a point source is blurred through an optical system. It can be viewed as the “impulse response” of an optical system.
  • Characteristic: In an ideal optical system, the PSF would be a delta function (point-like), but in a real system, the light source spreads in a blurry fashion (e.g., disk-like). LSF (Line Spread Function)
  • Definition: LSF represents the response of a line light source, instead of a point source, as it passes through an optical system. It can be viewed as a one-dimensional analog of PSF.
  • Calculation: The LSF is obtained as the integral of the PSF. It can also be calculated as the derivative of the edge spread function (ESF).

This page is auto-translated from /nishio/解像度と解像力の違い using DeepL. If you looks something interesting but the auto-translated English is not good enough to understand it, feel free to let me know at @nishio_en. I’m very happy to spread my thought to non-Japanese readers.