A Human-in-the-Loop Deep Learning Framework for Color Reconstruction of Lenticular Films
Historical lenticular films, such as those created with the Kodacolor process, encode color information in a distinctive spatial format. This structure requires specialized techniques for accurate color reconstruction. While recent signal processing approaches like doLCE and deep learning methods like deep-doLCE have advanced automated color recovery, they often fail with cases such as curved lenticules, low-contrast, or badly captured regions. We propose a human-in-the-loop (HITL) deep learning framework which is designed for color reconstruction in lenticular films. Our approach introduces an editable, vector-based representation of lenticule boundaries, allowing experts to interactivel...
| Authors | Saptarshi Neil Sinha, Tiago Kleist, Giorgio Trumpy |
|---|---|
| Primary category | cs.CV |
| Published | 3 August 2026 |
| Last updated at source | 5 August 2026 |
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