Motion blur in digital images - analys, detection and correction of motion blur in photogrammetry

Unmanned aerial vehicles (UAV) have become an interesting and active research topic for photogrammetry. Current research is based on images acquired by an UAV, which have a high ground resolution and good spectral and radiometrical resolution, due to the low flight altitudes combined with a high resolution camera. UAV image flights are also cost effective and have become attractive for many applications including, change detection in small scale areas. One of the main problems preventing full automation of data processing of UAV imagery is the degradation effect of blur caused by camera movement during image acquisition. This can be caused by the normal flight movement of the UAV as well as strong winds, turbulence or sudden operator inputs. This blur disturbs the visual analysis and interpretation of the data, causes errors and can degrade the accuracy in automatic photogrammetric processing algorithms. The detection and removal of these images is currently achieved manually, which is both time consuming and prone to error, particularly for large image-sets. To increase the quality of data processing an automated process is necessary, which must be both reliable and quick. This thesis proves the negative affect that blurred images have on photogrammetric processing. It shows that small amounts of blur do have serious impacts on target detection and that it slows down processing speed due to the requirement of human intervention. Larger blur can make an image completely unusable and needs to be excluded from processing. To exclude images out of large image datasets an algorithm was developed. The newly developed method makes it possible to detect blur caused by linear camera displacement. The method is based on human detection of blur. Humans detect blurred images best by comparing it to other images in order to establish whether an image is blurred or not. The developed algorithm simulates this procedure by creating an image for comparison using image processing. Creating internally a comparable image makes the method independent of additional images. However, the calculated blur value named SIEDS (saturation image edge difference standard-deviation) on its own does not provide an absolute number to judge if an image is blurred or not. To achieve a reliable judgement of image sharpness the SIEDS value has to be compared to other SIEDS values of the same dataset. This algorithm enables the exclusion of blurred images and subsequently allows photogrammetric processing without them. However, it is also possible to use deblurring techniques to restor blurred images. Deblurring of images is a widely researched topic and often based on the Wiener or Richardson-Lucy deconvolution, which require precise knowledge of both the blur path and extent. Even with knowledge about the blur kernel, the correction causes errors such as ringing, and the deblurred image appears muddy and not completely sharp. In the study reported in this paper, overlapping images are used to support the deblurring process. An algorithm based on the Fourier transformation is presented. This works well in flat areas, but the need for geometrically correct sharp images for deblurring may limit the application. Another method to enhance the image is the unsharp mask method, which improves images significantly and makes photogrammetric processing more successful. However, deblurring of images needs to focus on geometric correct deblurring to assure geometric correct measurements. Furthermore, a novel edge shifting approach was developed which aims to do geometrically correct deblurring. The idea of edge shifting appears to be promising but requires more advanced programming

Data-Driven Image Restoration

Every day many images are taken by digital cameras, and people are demanding visually accurate and pleasing result. Noise and blur degrade images captured by modern cameras, and high-level vision tasks (such as segmentation, recognition, and tracking) require high-quality images. Therefore, image restoration specifically, image deblurring and image denoising is a critical preprocessing step. A fundamental problem in image deblurring is to recover reliably distinct spatial frequencies that have been suppressed by the blur kernel. Existing image deblurring techniques often rely on generic image priors that only help recover part of the frequency spectrum, such as the frequencies near the high-end. To this end, we pose the following specific questions: (i) Does class-specific information offer an advantage over existing generic priors for image quality restoration? (ii) If a class-specific prior exists, how should it be encoded into a deblurring framework to recover attenuated image frequencies? Throughout this work, we devise a class-specific prior based on the band-pass filter responses and incorporate it into a deblurring strategy. Specifically, we show that the subspace of band-pass filtered images and their intensity distributions serve as useful priors for recovering image frequencies. Next, we present a novel image denoising algorithm that uses external, category specific image database. In contrast to existing noisy image restoration algorithms, our method selects clean image “support patches” similar to the noisy patch from an external database. We employ a content adaptive distribution model for each patch where we derive the parameters of the distribution from the support patches. Our objective function composed of a Gaussian fidelity term that imposes category specific information, and a low-rank term that encourages the similarity between the noisy and the support patches in a robust manner. Finally, we propose to learn a fully-convolutional network model that consists of a Chain of Identity Mapping Modules (CIMM) for image denoising. The CIMM structure possesses two distinctive features that are important for the noise removal task. Firstly, each residual unit employs identity mappings as the skip connections and receives pre-activated input to preserve the gradient magnitude propagated in both the forward and backward directions. Secondly, by utilizing dilated kernels for the convolution layers in the residual branch, each neuron in the last convolution layer of each module can observe the full receptive field of the first layer

A review of spatial enhancement of hyperspectral remote sensing imaging techniques

Remote sensing technology has undeniable importance in various industrial applications, such as mineral exploration, plant detection, defect detection in aerospace and shipbuilding, and optical gas imaging, to name a few. Remote sensing technology has been continuously evolving, offering a range of image modalities that can facilitate the aforementioned applications. One such modality is Hyperspectral Imaging (HSI). Unlike Multispectral Images (MSI) and natural images, HSI consist of hundreds of bands. Despite their high spectral resolution, HSI suffer from low spatial resolution in comparison to their MSI counterpart, which hinders the utilization of their full potential. Therefore, spatial enhancement, or Super Resolution (SR), of HSI is a classical problem that has been gaining rapid attention over the past two decades. The literature is rich with various SR algorithms that enhance the spatial resolution of HSI while preserving their spectral fidelity. This paper reviews and discusses the most important algorithms relevant to this area of research between 2002-2022, along with the most frequently used datasets, HSI sensors, and quality metrics. Meta-analysis are drawn based on the aforementioned information, which is used as a foundation that summarizes the state of the field in a way that bridges the past and the present, identifies the current gap in it, and recommends possible future directions

Reconstruction, Classification, and Segmentation for Computational Microscopy

This thesis treats two fundamental problems in computational microscopy: image reconstruction for magnetic resonance force microscopy (MRFM) and image classification for electron backscatter diffraction (EBSD). In MRFM, as in many inverse problems, the true point spread function (PSF) that blurs the image may be only partially known. The image quality may suffer from this possible mismatch when standard image reconstruction techniques are applied. To deal with the mismatch, we develop novel Bayesian sparse reconstruction methods that account for possible errors in the PSF of the microscope and for the inherent sparsity of MRFM images. Two methods are proposed: a stochastic method and a variational method. They both jointly estimate the unknown PSF and unknown image. Our proposed framework for reconstruction has the flexibility to incorporate sparsity inducing priors, thus addressing ill-posedness of this non-convex problem, Markov-Random field priors, and can be extended to other image models. To obtain scalable and tractable solutions, a dimensionality reduction technique is applied to the highly nonlinear PSF space. The experiments clearly demonstrate that the proposed methods have superior performance compared to previous methods. In EBSD we develop novel and robust dictionary-based methods for segmentation and classification of grain and sub-grain structures in polycrystalline materials. Our work is the first in EBSD analysis to use a physics-based forward model, called the dictionary, to use full diffraction patterns, and that efficiently classifies patterns into grains, boundaries, and anomalies. In particular, unlike previous methods, our method incorporates anomaly detection directly into the segmentation process. The proposed approach also permits super-resolution of grain mantle and grain boundary locations. Finally, the proposed dictionary-based segmentation method performs uncertainty quantification, i.e. p-values, for the classified grain interiors and grain boundaries. We demonstrate that the dictionary-based approach is robust to instrument drift and material differences that produce small amounts of dictionary mismatch.PhDElectrical Engineering: SystemsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/102296/1/seunpark_1.pd

ISIPTA'07: Proceedings of the Fifth International Symposium on Imprecise Probability: Theories and Applications

B

Quinoa phenotyping methodologies: An international consensus

Quinoa is a crop originating in the Andes but grown more widely and with the genetic potential for significant further expansion. Due to the phenotypic plasticity of quinoa, varieties need to be assessed across years and multiple locations. To improve comparability among field trials across the globe and to facilitate collaborations, components of the trials need to be kept consistent, including the type and methods of data collected. Here, an internationally open-access framework for phenotyping a wide range of quinoa features is proposed to facilitate the systematic agronomic, physiological and genetic characterization of quinoa for crop adaptation and improvement. Mature plant phenotyping is a central aspect of this paper, including detailed descriptions and the provision of phenotyping cards to facilitate consistency in data collection. High-throughput methods for multi-temporal phenotyping based on remote sensing technologies are described. Tools for higher-throughput post-harvest phenotyping of seeds are presented. A guideline for approaching quinoa field trials including the collection of environmental data and designing layouts with statistical robustness is suggested. To move towards developing resources for quinoa in line with major cereal crops, a database was created. The Quinoa Germinate Platform will serve as a central repository of data for quinoa researchers globally.Fil: Stanschewski, Clara S.. King Abdullah University of Science and Technology; Arabia SauditaFil: Rey, Elodie. King Abdullah University of Science and Technology; Arabia SauditaFil: Fiene, Gabriele. King Abdullah University of Science and Technology; Arabia SauditaFil: Craine, Evan B.. Washington State University; Estados UnidosFil: Wellman, Gordon. King Abdullah University of Science and Technology; Arabia SauditaFil: Melino, Vanessa J.. King Abdullah University of Science and Technology; Arabia SauditaFil: Patiranage, Dilan S. R.. King Abdullah University of Science and Technology; Arabia SauditaFil: Johansen, Kasper. King Abdullah University of Science and Technology; Arabia SauditaFil: Schmöckel, Sandra M.. King Abdullah University of Science and Technology; Arabia SauditaFil: Bertero, Hector Daniel. Universidad de Buenos Aires. Facultad de Agronomía. Departamento de Producción Vegetal. Cátedra de Producción Vegetal; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigaciones Fisiológicas y Ecológicas Vinculadas a la Agricultura. Universidad de Buenos Aires. Facultad de Agronomía. Instituto de Investigaciones Fisiológicas y Ecológicas Vinculadas a la Agricultura; ArgentinaFil: Oakey, Helena. University of Adelaide; AustraliaFil: Colque Little, Carla. Universidad de Copenhagen; DinamarcaFil: Afzal, Irfan. University of Agriculture; PakistánFil: Raubach, Sebastian. The James Hutton Institute; Reino UnidoFil: Miller, Nathan. University of Wisconsin; Estados UnidosFil: Streich, Jared. Oak Ridge National Laboratory; Estados UnidosFil: Amby, Daniel Buchvaldt. Universidad de Copenhagen; DinamarcaFil: Emrani, Nazgol. Christian-albrechts-universität Zu Kiel; AlemaniaFil: Warmington, Mark. Agriculture And Food; AustraliaFil: Mousa, Magdi A. A.. Assiut University; Arabia Saudita. King Abdullah University of Science and Technology; Arabia SauditaFil: Wu, David. Shanxi Jiaqi Agri-Tech Co.; ChinaFil: Jacobson, Daniel. Oak Ridge National Laboratory; Estados UnidosFil: Andreasen, Christian. Universidad de Copenhagen; DinamarcaFil: Jung, Christian. Christian-albrechts-universität Zu Kiel; AlemaniaFil: Murphy, Kevin. Washington State University; Estados UnidosFil: Bazile, Didier. Savoirs, Environnement, Sociétés; Francia. Universite Paul-valery Montpellier Iii; FranciaFil: Tester, Mark. King Abdullah University of Science and Technology; Arabia Saudit

Quinoa Phenotyping Methodologies: An International Consensus

Quinoa is a crop originating in the Andes but grown more widely and with the genetic potential for significant further expansion. Due to the phenotypic plasticity of quinoa, varieties need to be assessed across years and multiple locations. To improve comparability among field trials across the globe and to facilitate collaborations, components of the trials need to be kept consistent, including the type and methods of data collected. Here, an internationally open-access framework for phenotyping a wide range of quinoa features is proposed to facilitate the systematic agronomic, physiological and genetic characterization of quinoa for crop adaptation and improvement. Mature plant phenotyping is a central aspect of this paper, including detailed descriptions and the provision of phenotyping cards to facilitate consistency in data collection. High-throughput methods for multi-temporal phenotyping based on remote sensing technologies are described. Tools for higher-throughput post-harvest phenotyping of seeds are presented. A guideline for approaching quinoa field trials including the collection of environmental data and designing layouts with statistical robustness is suggested. To move towards developing resources for quinoa in line with major cereal crops, a database was created. The Quinoa Germinate Platform will serve as a central repository of data for quinoa researchers globally.EEA FamailláFil: Stanschewski, Clara S. King Abdullah University of Science and Technology. Center for Desert Agriculture, Biological and Environmental Sciences and Engineering Division; Arabia SauditaFil: Rey, Elodie. King Abdullah University of Science and Technology. Center for Desert Agriculture, Biological and Environmental Sciences and Engineering Division; Arabia SauditaFil: Fiene, Gabriele. King Abdullah University of Science and Technology. Center for Desert Agriculture, Biological and Environmental Sciences and Engineering Division; Arabia SauditaFil: Craine, Evan B. Washington State University. Department of Crop and Soil Sciences; Estados UnidosFil: Wellman, Gordon. King Abdullah University of Science and Technology. Center for Desert Agriculture, Biological and Environmental Sciences and Engineering Division; Arabia SauditaFil: Melino, Vanessa J. King Abdullah University of Science and Technology. Center for Desert Agriculture, Biological and Environmental Sciences and Engineering Division; Arabia SauditaFil: Patiranage, Dilan S.R. King Abdullah University of Science and Technology. Center for Desert Agriculture, Biological and Environmental Sciences and Engineering Division; Arabia SauditaFil: Patiranage, Dilan S.R. Christian-Albrechts-University of Kiel. Plant Breeding Institute; AlemaniaFil: Johansen, Kasper. King Abdullah University of Science and Technology. Water Desalination and Reuse Center; Arabia SauditaFil: Schmöckel, Sandra M. University of Hohenheim. Institute of Crop Science. Department Physiology of Yield Stability; AlemaniaFil: Erazzu, Luis Ernesto. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Famaillá; Argentina.Fil: Tester, Mark. King Abdullah University of Science and Technology. Center for Desert Agriculture, Biological and Environmental Sciences and Engineering Division; Arabia Saudit