{"type":"ScholarlyArticle","title":"Diffract and Conquer: Hyperspectral Imaging from Any RGB Camera via Optical Encoding and Learning","authors":[{"name":"Alexey Pronin","slug":"alexey-pronin","given":"Alexey","family":"Pronin","affiliation":""},{"name":"Daniil Vladimirov","slug":"daniil-vladimirov","given":"Daniil","family":"Vladimirov","affiliation":""},{"name":"Andrei Korepanov","slug":"andrei-korepanov","given":"Andrei","family":"Korepanov","affiliation":""},{"name":"Artem Muzyka","slug":"artem-muzyka","given":"Artem","family":"Muzyka","affiliation":""},{"name":"Andrey Makarov","slug":"andrey-makarov","given":"Andrey","family":"Makarov","affiliation":""},{"name":"Sofya Podtikhova","slug":"sofya-podtikhova","given":"Sofya","family":"Podtikhova","affiliation":""},{"name":"Egor Ershov","slug":"egor-ershov","given":"Egor","family":"Ershov","affiliation":""},{"name":"Andrey Rastorguev","slug":"andrey-rastorguev","given":"Andrey","family":"Rastorguev","affiliation":""},{"name":"Roman Skidanov","slug":"roman-skidanov","given":"Roman","family":"Skidanov","affiliation":""},{"name":"Artem Nikonorov","slug":"artem-nikonorov","given":"Artem","family":"Nikonorov","affiliation":""}],"abstract":"Hyperspectral imaging (HSI) provides detailed spectral information but is often impractical due to high cost, size, and hardware complexity. While learning-based methods attempt to recover hyperspectral images from RGB sensors, they are constrained by limited spectral measurements and noise. We propose a unified optical–computational approach that converts any standard RGB camera into a snapshot hyperspectral imaging system. Our method introduces a diffractive optical adapter that replaces the conventional lens with a diffractive lens array optimized for spectral encoding. To reconstruct hyperspectral images from the resulting measurements, we design a neural network specialized for diffractively encoded RGB data, capable of compensating for optical distortions and recovering high-quality spectra. The proposed system achieves up to 10 dB improvement in PSNR over RGB-based hyperspectral reconstruction and enhances the performance of existing state-of-theart models by up to 6 dB when used with the proposed adapter. Our results demonstrate that diffractive optical encoding combined with learned reconstruction enables practical and scalable hyperspectral imaging using commodity RGB cameras.","doi":"10.24963/ijcai.2026/170","url":"https://ijcai.org/proceedings/2026/170","pdf":"https://ijcai.org/proceedings/2026/0170.pdf","markdown":"https://ijcai.org/proceedings/2026/170.md","bibtex":"https://ijcai.org/proceedings/2026/bibtex/170","volume":{"slug":"2026","title":"Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2026","url":"https://ijcai.org/proceedings/2026/","year":2026,"location":"Bremen, Germany","dates":"15-21 August 2026","editors":"Diego Calvanese","isbn":"978-1-956792-09-6","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2026"},"section":"Computer Vision","track":"Main Track","number":170,"pages":{"first":1523,"last":1531},"keywords":["Computer Vision: Low-level Vision","Computer Vision: Applications and Systems","Computer Vision: Computational photography"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}