The COVID-19 (Coronavirus Disease 2019), caused by the SARS-CoV-2 virus, can cause severe pneumonia. The chest X-ray (CXR) is the preliminary radiographic exam to investigate pneumonia and other lung abnormalities. However, the identification of opacities in the CXR is challenging due to its overall low quality. In the literature, several works have proposed automated approaches to improve the quality of CXR images in general. However, as far as we know, few studies explored them in the context of COVID-19 identification in a thorough manner. In this work, we applied a series of digital image processing techniques, focusing on contrast enhancement. The impact of each method was evaluated using traditional digital image quality metrics, specifically peak signal-to-noise ratio (PSNR) and universal image quality (UQI), and a deep learning model for COVID-19 detection. We also investigated which regions were most frequently used by the model after each transformation using an Explainable Artificial Intelligence (XAI) approach called LIME. The rationale is to evaluate if any transformation improves the lung region used by the recognition model. The enhancement strategies that presented the overall best results considering PSNR and UQI were Gamma Correction (GC) and Adaptive Gamma Correction With Weighting Distribution (AGCWD). However, no method improved the identification of COVID-19 in a significant matter.

Static and Dynamic Point Cloud Compression by TSPLVQ


**Session chairs:**  Aura Conci and Jarmila Pavlovičová

Session: Image and Video Processing I

technical paper


IWSSIP is an international conference that brings together researchers and developers from both academia and industry reporting the latest scientific and theoretical advances to discuss and debate major issues and state-of-the-art aspects on systems, signals and image processing.

###### For the session discussion, each author will be seated at individual breakout tables.  Please find which table they will be at here:
###### [QA table assignments in Gather.town](https://drive.google.com/file/d/1hBHOD_KF7NSsaTcpYcHCZslXJvQVxJQe/view?usp=sharing)

Amira Filali

1

Presentations

Static and Dynamic Point Cloud Compression by TSPLVQ

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