Resolution Approximation Methods (RAM) play a crucial role in many real-world applications where preserving the original image quality is essential. Depending on the specific applicative field, the approximation may focus on spatial and/or color (intensity) information [7], [6]. Over the years, several methods have been proposed for color (gray) images, and multiple research directions have been pursued to enhance the performance and robustness of RAM [1],[2], [3], [4] and [5]. This contribution explores some approaches for both spatial and color (intensity) resolution approximation, providing a comprehensive analysis of their benefits, drawbacks, and potential future advancements.
Resolution Approximation Methods for Image Processing Applications
Ramella G
2023
Abstract
Resolution Approximation Methods (RAM) play a crucial role in many real-world applications where preserving the original image quality is essential. Depending on the specific applicative field, the approximation may focus on spatial and/or color (intensity) information [7], [6]. Over the years, several methods have been proposed for color (gray) images, and multiple research directions have been pursued to enhance the performance and robustness of RAM [1],[2], [3], [4] and [5]. This contribution explores some approaches for both spatial and color (intensity) resolution approximation, providing a comprehensive analysis of their benefits, drawbacks, and potential future advancements.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
