Advanced Filters for Photo Restoration: Part 1
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
As you have seen in Chapter 3 , Photoshop’s Filter dropdown menu has a variety of filters for basic correction and artistic effects. However, as you saw with the Blur Gallery filter, some filters are combined into one workspace area. This is true of other advanced filters that we will be looking at in this chapter and later in Chapter 5 . Some are specifically for correcting distortion in a photo, and others blur the line between restoration and adding an artistic effect. Many of these filters have been in Photoshop for many years, while others have been recently added or updated. In this chapter we will be taking an overview look at certain advanced filters that would normally be used for restoration and color correction in professional digital photography. However, I will be pointing out sections of these filters that you may want to experiment with on your scanned images as an alternative to the basic filters that were discussed in the previous chapter.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it