Image Super-Resolution Enhancement Algorithm Based on Edge Features
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This research proposes an image super-resolution enhancement algorithm based on edge features, designed to improve image resolution. The algorithm analyzes edge information within images and employs advanced image processing techniques to perform upscaling, resulting in clearer images with richer details. Key implementation aspects include edge detection using operators like Sobel or Canny, followed by edge-aware interpolation methods such as guided filtering or deep learning-based approaches to preserve sharp boundaries during magnification. Through this algorithm, image quality can be effectively improved, enhancing visual effects and meeting user demands for high-quality images. Additionally, the algorithm demonstrates practical utility and promising application prospects, holding broad value in fields like medical imaging, surveillance systems, and digital media processing where edge preservation is critical for visual fidelity.
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