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2. DeFlow: Learning Complex Image Degradations from Unpaired Data with Conditional Flows

Author

Wolf, Valentin, Lugmayr, Andreas, Danelljan, Martin, Van Gool, Luc, and Timofte, Radu

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning, Electrical Engineering and Systems Science - Image and Video Processing
Abstract

The difficulty of obtaining paired data remains a major bottleneck for learning image restoration and enhancement models for real-world applications. Current strategies aim to synthesize realistic training data by modeling noise and degradations that appear in real-world settings. We propose DeFlow, a method for learning stochastic image degradations from unpaired data. Our approach is based on a novel unpaired learning formulation for conditional normalizing flows. We model the degradation process in the latent space of a shared flow encoder-decoder network. This allows us to learn the conditional distribution of a noisy image given the clean input by solely minimizing the negative log-likelihood of the marginal distributions. We validate our DeFlow formulation on the task of joint image restoration and super-resolution. The models trained with the synthetic data generated by DeFlow outperform previous learnable approaches on three recent datasets. Code and trained models are available at: https://github.com/volflow/DeFlow, Comment: CVPR 2021 Oral

Published
2021

4. Molecular Surveillance, Prevalence, and Distribution of Cacao Infecting Badnavirus Species in Côte d'Ivoire and Ghana.

Author

Ameyaw, George A., Kouakou, Koffié, Iqbal, Mohammed Javed, Belé, Luc, Wolf, Valentin L. F., Keith, Cory V., Bi, Bolou A. Bolou, Kouamé, Christophe, Livingstone, Donald, Domfeh, Owusu, Gyamera, Ebenezer A., Marelli, Jean-Philippe, and Brown, Judith K.

Subjects
CACAO, SPECIES, BORDERLANDS, PROTEIN domains, CACAO beans, VIRAL proteins
Abstract

The cacao swollen shoot disease (CSSD) caused by a complex of badnavirus species presents a major challenge for cacao production in West Africa, especially Ghana and Côte d'Ivoire. In this study, CSSD species detection efficiency, diversity, and geographic distribution patterns in cacao plantations in Ghana and Côte d'Ivoire were investigated through field surveillance, PCR detection assays, sequencing of positive amplicons, and phylogeographic clustering. Cumulatively, the detection efficiency of the tested CSSD primer sets that were targeting the movement protein domain of the virus ranged from 0.15% (CSSD-3 primer) to 66.91% (CSSD-1 primer) on all the symptomatic cacao leaf samples assessed. The identified CSSD species differed phylogenetically and overlapped in distribution, with the cacao swollen shoot Togo B virus (CSSTBV) (n = 588 sequences) being the most prevalent and widely distributed compared to the other CSSD species that were encountered in both countries. Geographically, the cacao swollen shoot CE virus (CSSCEV) species (n = 124 sequences) that was identified was largely restricted to the bordering regions of Ghana and Côte d'Ivoire. These results provide updated knowledge of the geographic distribution of the key CSSD species and their diagnostic efficiency and, thus, provide guidance in identifying locations for structured testing of cacao germplasm and optimal diagnostics for the predominant CSSD species in Ghana and Côte d'Ivoire. [ABSTRACT FROM AUTHOR]

Published
2024
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