The article discusses image noise â random variations in brightness or color â primarily stemming from sensor errors in digital photography, particularly in low light. It explains how noise affects image quality and contrasts it with analog film grain. The tutorial focuses on noise reduction techniques, emphasizing the importance of maintaining original image fidelity during the denoising process. It outlines the use of OpenCV algorithms within a Streamlit app, allowing users to apply various noise reduction methods and interactively adjust settings. Users will learn to create web applications with Streamlit while processing images effectively.
Image noise is a random variation of brightness or color, often complicating detail discernment in photos. It emerges from sensor electronic noise, particularly in low light.
Denoising improves visual appeal and feature identification in images. While removing noise, we must keep the denoised image an accurate reflection of the original.
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