How Image Reconstruction is Different from Image Restoration?
Image reconstruction differs from image restoration primarily in the nature of the input data and the process used to obtain it.
Image restoration starts with a degraded version of an image that already exists, aiming to improve its quality by undoing degradations like blur or noise. Image reconstruction, conversely, builds an image from non-image data, which are typically measurements resulting from the interaction of energy with the object being imaged.
Key Differences Between Image Restoration and Image Reconstruction
| Feature | Image Restoration | Image Reconstruction |
|---|---|---|
| Input Data | A pre-existing degraded image. | Measurements, signals, or projections generated by energy interacting with an object. |
| Goal | Improve the quality of a degraded image (e.g., deblurring, denoising). | Create a viewable image representation of an object or scene from raw data. |
| Observation Process | The input image is a degraded version of the unknown object itself. Reference: In image restoration, the unknown object and its degraded observed version, re- ferred to as the image, are both scalar functions defined on FI2 or Z2. |
Observations result from energy (like a wave) interacting with the unknown object. Reference: In image reconstruction, observations result from the interaction between the unknown object and some scattering wave. |
| Problem Type | Typically an inverse problem trying to undo known or estimated degradations. | Typically an inverse problem trying to infer the object's properties from indirect measurements. |
| Examples | Removing motion blur from a photo, reducing noise in a scan, sharpening an image. | Creating a CT scan slice from X-ray projections, generating an MRI image, SAR imaging. |
Understanding Image Restoration
Image restoration is like trying to fix a damaged photograph. You start with a picture that's already blurry, noisy, or has artifacts, and your goal is to make it look as close to the original, undegraded scene as possible. The input itself is a form of the image, albeit imperfect.
- Input: A visible image with quality issues.
- Process: Techniques involve modeling the degradation process (e.g., how motion caused blur, how the sensor added noise) and then applying algorithms to reverse or compensate for that degradation.
- Reference Insight: As the reference states, both the desired original object and the input degraded image are treated as scalar functions representing intensity values on a grid (FI2 or Z2).
Understanding Image Reconstruction
Image reconstruction is fundamentally different; it's more like piecing together a picture from puzzle pieces that aren't part of the final image itself. Instead of starting with a blurry image, you start with data collected by sensors interacting with an object.
- Input: Raw data, often measurements from sensors (like X-ray attenuation, radio frequency signals, or reflected sound waves). This data isn't a direct image.
- Process: Algorithms process this non-image data to mathematically build or reconstruct the visual representation of the object or scene.
- Reference Insight: The reference highlights that the observations don't come from a degraded image directly, but result from the interaction between the unknown object and some scattering wave. This interaction data is what fuels the reconstruction process.
In essence, restoration fixes an existing flawed image, while reconstruction creates an image from indirect measurements of an object's properties.
