Introduction to Image Formation
1. Overview of Image Formation
Image formation refers to the conversion of visual information from the physical world into a digital image. It is the first and foundational step for any computer vision or digital image processing task.
2. Stages of Image Formation
a) Physical Scene
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The real-world environment or objects that are to be captured.
b) Illumination
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Light plays a crucial role in how scenes appear.
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Different sources (natural vs. artificial) affect color, brightness, and shadow.
c) Camera Optics (Lens System)
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Lens: Focuses incoming light onto the sensor surface.
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Aperture: Regulates the amount of light by changing its size.
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Shutter: Controls the exposure duration—how long light hits the sensor.
d) Image Sensor
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The sensor (CCD - Charge-Coupled Device or CMOS - Complementary metal-oxide-Semiconductor) turns photons (light) into electrical signals.
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Each pixel on the sensor collects light and converts it into a voltage proportional to intensity.
e) Sampling and Quantization
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The continuous image formed on the sensor is sampled into discrete points (pixels).
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Each pixel's analog value is quantized (converted) into a digital number, e.g., 0–255 in 8-bit depth.
3. Key Steps: Flowchart Representation
Stage | What Happens |
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Physical Scene | Objects exist in presence of light |
Illumination | Scene is lit by natural or artificial sources |
Camera & Lens | Light is focused and managed onto the sensor |
Sensor | Light intensity is converted into analog voltage |
Digitization | Analog values are sampled & quantized to digital |
Image | Digital values form a pixel grid (the image) |
4. Types of Digital Images
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Grayscale – Each pixel represents brightness only (intensity).
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Color (RGB) – Three channels; each pixel stores values for Red, Green, Blue.
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Other formats – Multispectral, infrared, thermal, etc., depending on the sensor.
5. Mathematical Models
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Pinhole Camera Model: Projects 3D world into a 2D image using simple geometry:
where is focal length; are real-world coordinates.
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Lens Camera Model: Incorporates focus and distortion characteristics for realistic image models.
6. Influence Factors
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Noise: Caused by sensor imperfections or low light.
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Blur: Owing to camera movement or defocus.
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Lens Distortion: Straight lines appear curved due to optical properties.
7. Applications & Importance
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Understanding these steps is essential for any task in computer vision:
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Pre-processing: Denoising, deblurring, color balancing depend on image formation knowledge.
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Scene Understanding: Knowing formation helps with meaningful interpretation.
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System Design: Camera choice, sensor type, and pre/post-processing all rely on these fundamentals.
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8. Summary
Understanding image formation is crucial as all later processing depends on the quality and nature of the acquired image. Mastery of this topic ensures robust performance in computer vision, image enhancement, and analysis.
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