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Методы ускорения сбора данных в системах однопиксельной визуализации
В.С. Шумигай 1, А.К. Лаппо-Данилевская 1, А.С. Синько 2,3, Д.П. Агапов 2, А.О. Исмагилов 1, Е.Н. Опарин 1, А.Н. Цыпкин 1

1 Университет ИТМО,
197101, Россия, г. Санкт-Петербург, Кронверкский пр., д. 49, лит. А;
2 Московский государственный университет им. М.В. Ломоносова,
119991, Россия, г. Москва, ул. Ленинские Горы, д. 1 стр. 2;
3 Национальный исследовательский центр «Курчатовский институт»,
123182, Россия, г. Москва, пл. Академика Курчатова, д. 1

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DOI: 10.18287/COJ1753

Страницы: 1213-1227.

Аннотация:
В работе представлен обзор существующих методов ускорения сбора данных в системах однопиксельной визуализации, включая однопиксельные камеры и фантомную визуализацию. Рассмотрены три ключевых подхода к ускорению сбора данных, а именно: мультиплексирование, оптимизация паттернов и использование обратной связи от детектора. Первый подход – мультиплексирование паттернов – позволяет параллельно обрабатывать данные в различных спектральных, поляризационных или временных каналах. Второй подход – оптимизация паттернов – направлен на сокращение числа измерений без потери качества изображения. В работе рассмотрены случайные, ортогональные (например, Адамара и Фурье) и модифицированные паттерны для ускорения процесса сбора данных. Третий подход – использование обратной связи от детектора – обеспечивает адаптивную корректировку паттернов, что повышает скорость и точность восстановления изображений. Этот подход особенно эффективен в сочетании с нейронными сетями. Рассмотренные методы подчеркивают актуальность разработки высокоскоростных систем однопиксельной визуализации, которые применяются в дистанционном зондировании, медицине и других областях. Комбинация рассмотренных подходов открывает новые возможности для создания систем визуализации, работающих в режиме реального времени.

Ключевые слова:
однопиксельная визуализация, фантомная визуализация, фантомная поляриметрия, мультиплексирование, паттерны освещения, паттерны Адамара, коэффициент сжатия, нейронная сеть, машинное обучение.

Благодарности
Разделы «Введение», «Спектральное, поляризационное и временное мультиплексирование» и «Оптимизация паттернов освещения» подготовлены авторами В.С. Шумигай, А.К. Лаппо-Данилевская, А.О. Исмагилов, Е.Н. Опарин и А.Н. Цыпкин при поддержке Государственного задания № FSER-2025-0007. Раздел «Адаптивная однопиксельная визуализация» подготовлен автором А.С. Синько в рамках государственного задания НИЦ «Курчатовский институт». Раздел «Заключение» подготовлен автором Д.П. Агапов.

Цитирование:
Шумигай, В.С. Методы ускорения сбора данных в системах однопиксельной визуализации / В.С. Шумигай, А.К. Лаппо-Данилевская, А.С. Синько, Д.П. Агапов, А.О. Исмагилов, Е.Н. Опарин, А.Н. Цыпкин // Компьютерная оптика. – 2025. – Т. 49, № 6. – С. 1213-1227. – DOI: 10.18287/COJ1753.

Citation:
Shumigai VS, Lappo-Danilevskaya AK, Sinko AS, Agapov DP, Ismagilov AO, Oparin EN, Tcypkin AN. Methods for accelerating data acquisition in single-pixel imaging systems. Computer Optics 2025; 49(6): 1213-1227. DOI: 10.18287/COJ1753.

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