Enhancing Image Quality: A Comparative Study of Spatial, Frequency Domain, and Deep Learning Methods

Image restoration and noise reduction methods have been created to restore deteriorated images and improve their quality. These methods have garnered substantial significance in recent times, mainly due to the growing utilization of digital imaging across diverse domains, including but not limited to medical imaging, surveillance, satellite imaging, and numerous others. In this paper, we conduct a comparative analysis of three distinct approaches to image restoration: the spatial method, the frequency domain method, and the deep learning method. The study was conducted on a dataset of 10,000 images, and the performance of each method was evaluated using the accuracy and loss metrics. The results show that the deep learning method outperformed the other two methods, achieving a validation accuracy of 72.68% after 10 epochs. The spatial method had the lowest accuracy of the three, achieving a validation accuracy of 69.98% after 10 epochs. The FFT frequency domain method had a validation accuracy of 52.87% after 10 epochs, significantly lower than the other two methods. The study demonstrates that deep learning is a promising approach for image classification tasks and outperforms traditional methods such as spatial and frequency domain techniques

Notes for genera – Ascomycota

Knowledge of the relationships and thus the classification of fungi, has developed rapidly with increasingly widespread use of molecular techniques, over the past 10--15 years, and continues to accelerate. Several genera have been found to be polyphyletic, and their generic concepts have subsequently been emended. New names have thus been introduced for species which are phylogenetically distinct from the type species of particular genera. The ending of the separate naming of morphs of the same species in 2011, has also caused changes in fungal generic names. In order to facilitate access to all important changes, it was desirable to compile these in a single document. The present article provides a list of generic names of Ascomycota (approximately 6500 accepted names published to the end of 2016), including those which are lichen-forming. Notes and summaries of the changes since the last edition of `Ainsworth Bisby's Dictionary of the Fungi' in 2008 are provided. The notes include the number of accepted species, classification, type species (with location of the type material), culture availability, life-styles, distribution, and selected publications that have appeared since 2008. This work is intended to provide the foundation for updating the ascomycete component of the ``Without prejudice list of generic names of Fungi'' published in 2013, which will be developed into a list of protected generic names. This will be subjected to the XIXth International Botanical Congress in Shenzhen in July 2017 agreeing to a modification in the rules relating to protected lists, and scrutiny by procedures determined by the Nomenclature Committee for Fungi (NCF). The previously invalidly published generic names Barriopsis, Collophora (as Collophorina), Cryomyces, Dematiopleospora, Heterospora (as Heterosporicola), Lithophila, Palmomyces (as Palmaria) and Saxomyces are validated, as are two previously invalid family names, Bartaliniaceae and Wiesneriomycetaceae. Four species of Lalaria, which were invalidly published are transferred to Taphrina and validated as new combinations. Catenomycopsis Tibell Constant. is reduced under Chaenothecopsis Vain., while Dichomera Cooke is reduced under Botryosphaeria Ces. De Not. (Art. 59)