Systematic approach to nonlinear filtering associated with aggregation operators. Part 2. Frechet MIMO-filters

Median filtering has been widely used in scalar-valued image processing as an edge preserving operation. The basic idea is that the pixel value is replaced by the median of the pixels contained in a window around it. In this work, this idea is extended onto vector-valued images. It is based on the fact that the median is also the value that minimizes the sum of distances between all grey-level pixels in the window. The Frechet median of a discrete set of vector-valued pixels in a metric space with a metric is the point minimizing the sum of metric distances to the all sample pixels. In this paper, we extend the notion of the Frechet median to the general Frechet median, which minimizes the Frechet cost function (FCF) in the form of aggregation function of metric distances, instead of the ordinary sum. Moreover, we propose use an aggregation distance instead of classical metric distance. We use generalized Frechet median for constructing new nonlinear Frechet MIMO-filters for multispectral image processing. (C) 2017 The Authors. Published by Elsevier Ltd.This work was supported by grants the RFBR No 17-07-00886, No 17-29-03369 and by Ural State Forest University Engineering's Center of Excellence in "Quantum and Classical Information Technologies for Remote Sensing Systems"

Агрегационный подход к нелинейной фильтрации. Часть 2. MIMO-фильтры

В этой статье мы расширяем понятие медианы Фреше до обобщенной медианы, которая минимизирует стоимостную функцию Фреше в форме агрегационной функции (вместо тривиальной суммы) от расстояний. Мы используем обобщенную медиану для конструирования новых нелинейных Фреше MIMO-фильтров для обработки многоканальных изображений.In this paper, we extend the notion of the Fréchet median to the general Fréchet median, which minimizes the Fréchet cost function (FCF) in the form of aggregation of metric distances, instead of the ordinary sum. Moreover, we propose use an aggregation distance instead of classical metric distance. We use generalized Fréchet median for constructing new nonlinear Fréchet MIMO-filters for multispectral image processing

Many factor mimo-filters

Исследуется эффективность модифицированных многофакторных (би-, три- и четырех-латеральных) MIMO-фильтров для серых, цветных и гиперспектральных изображений. Обычные билатеральные фильтры осуществляют взвешенное усреднение соседних пикселей. Веса включают две компоненты: пространственную и радиометрическую. Первая компонента учитывает геометрическое расстояние между центральным пикселем маски и его локальными соседями. Второй компонент учитывает радиометрическое расстояние между центральным пикселем маски и его локальными соседями. В этом классическом алгоритме центральный пиксель маски играет определяющую роль в конечном результате фильтрации. Если он искажен, то и результат фильтрации будет искаженным. Этот факт определяет первую модификацию: центральный пиксель заменяется его любой сглаженной версией, полученной на основе соседних пикселей. Вторая модификация использует матрично-значные веса. Они включают четыре компоненты: пространственную, радиометрическую, межканальную и радиометрическую межканальную. Четвертый вес учитывает радиометрическое расстояние между центральным пикселем и межканальными соседними пикселями

Systematic approach to nonlinear filtering associated with aggregation operators. Part 2. Fréchet MIMO-filters

Median filtering has been widely used in scalar-valued image processing as an edge preserving operation. The basic idea is that the pixel value is replaced by the median of the pixels contained in a window around it. In this work, this idea is extended onto vector-valued images. It is based on the fact that the median is also the value that minimizes the sum of distances between all grey-level pixels in the window. The Fréchet median of a discrete set of vector-valued pixels in a metric space with a metric is the point minimizing the sum of metric distances to the all sample pixels. In this paper, we extend the notion of the Fréchet median to the general Fréchet median, which minimizes the Fréchet cost function (FCF) in the form of aggregation function of metric distances, instead of the ordinary sum. Moreover, we propose use an aggregation distance instead of classical metric distance. We use generalized Fréchet median for constructing new nonlinear Fréchet MIMOfilters for multispectral image processing.This work was supported by grants the RFBR No. 17-07-00886 and by Ural State Forest Engineering’s Center of Excellence in ”Quantum and Classical Information Technologies for Remote Sensing Systems”

Recent Advances in Image Restoration with Applications to Real World Problems

In the past few decades, imaging hardware has improved tremendously in terms of resolution, making widespread usage of images in many diverse applications on Earth and planetary missions. However, practical issues associated with image acquisition are still affecting image quality. Some of these issues such as blurring, measurement noise, mosaicing artifacts, low spatial or spectral resolution, etc. can seriously affect the accuracy of the aforementioned applications. This book intends to provide the reader with a glimpse of the latest developments and recent advances in image restoration, which includes image super-resolution, image fusion to enhance spatial, spectral resolution, and temporal resolutions, and the generation of synthetic images using deep learning techniques. Some practical applications are also included

QMRNet: Quality Metric Regression for EO Image Quality Assessment and Super-Resolution

[EN] The latest advances in super-resolution have been tested with general-purpose images such as faces, landscapes and objects, but mainly unused for the task of super-resolving earth observation images. In this research paper, we benchmark state-of-the-art SR algorithms for distinct EO datasets using both full-reference and no-reference image quality assessment metrics. We also propose a novel Quality Metric Regression Network (QMRNet) that is able to predict the quality (as a no-reference metric) by training on any property of the image (e.g., its resolution, its distortions, etc.) and also able to optimize SR algorithms for a specific metric objective. This work is part of the implementation of the framework IQUAFLOW, which has been developed for the evaluation of image quality and the detection and classification of objects as well as image compression in EO use cases. We integrated our experimentation and tested our QMRNet algorithm on predicting features such as blur, sharpness, snr, rer and ground sampling distance and obtained validation medRs below 1.0 (out of N = 50) and recall rates above 95%. The overall benchmark shows promising results for LIIF, CAR and MSRN and also the potential use of QMRNet as a loss for optimizing SR predictions. Due to its simplicity, QMRNet could also be used for other use cases and image domains, as its architecture and data processing is fully scalable.The project was financed by the Ministry of Science and Innovation (MICINN) and by the European Union within the framework of FEDER RETOS-Collaboration of the State Program of Research (RTC2019-007434-7), Development and Innovation Oriented to the Challenges of Society, within the State Research Plan Scientific and Technical and Innovation 2017¿2020, with the main objective of promoting technological development, innovation and quality research.Berga, D.; Gallés, P.; Takáts, K.; Mohedano, E.; Riordan-Chen, L.; García-Moll, C.; Vilaseca, D.... (2023). QMRNet: Quality Metric Regression for EO Image Quality Assessment and Super-Resolution. Remote Sensing. 15(9). https://doi.org/10.3390/rs1509245115

Международная конференция и молодежная школа «Информационные технологии и нанотехнологии» (ИТНТ-2017)

В статье подведены итоги III Международной конференции и молодежной школы «Информационные технологии и нанотехнологии» (ИТНТ-2017), состоявшейся в Самаре 25-27 апреля 2017 года, а также кратко рассмотрена основная тематика исследований, обсуждаемых на ИТНТ-2017.Работа выполнена при поддержке Министерства образования и науки Российской Федерации