Reconfigurable Platform Pre-Processing MAC Unit Design: For Image Processing Core Architecture in Restoration Applications

The overwhelming majority of image processing algorithms are two-dimensional (2D) and, as a result, their scope is limited. As a result, the 2D convolution function has important implications for image processing needs. The 2D convolution and MAC design processes are used to perform image analysis tasks such as image blurring, softening, feature extraction, and image classification. This study’s primary goal is to develop a more efficient MAC control block-based architectural style for two-dimensional convolutions. In image processing applications, convolution deployment, the recommended 2D convolution architectural methodology, is significantly faster and requires far fewer hardware resources. The resulting convolution values are stored in memory when the convolution procedure is completed

Image Processing Using FPGAs

This book presents a selection of papers representing current research on using field programmable gate arrays (FPGAs) for realising image processing algorithms. These papers are reprints of papers selected for a Special Issue of the Journal of Imaging on image processing using FPGAs. A diverse range of topics is covered, including parallel soft processors, memory management, image filters, segmentation, clustering, image analysis, and image compression. Applications include traffic sign recognition for autonomous driving, cell detection for histopathology, and video compression. Collectively, they represent the current state-of-the-art on image processing using FPGAs