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Convolution Neural Network Approach for Single Image Super Resolution

Author

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  • Lajja Dave
  • Neha Patel
  • Nayana Suresh

Abstract

The goal of Super-Resolution (SR) is to generate a higher-resolution image from lower-resolution input images. High-resolution images offer more pixel density, thus capturing finer details of the original scene. Single Image Super-Resolution (SISR) seeks to restore a high-resolution image from a single low-resolution input, which is a significant challenge in computer vision. This process involves using the low-resolution image as the input and the high-resolution image as the reference, with the SR model producing the predicted high-resolution output. This paper proposes a neural network-based approach utilizing convolutional layers to improve Peak Signal-to-Noise Ratio (PSNR) and reduce processing time compared to traditional methods. The architecture consists of a convolutional layer, a max-pooling layer, and a reconstruction layer.

Suggested Citation

  • Lajja Dave & Neha Patel & Nayana Suresh, 2024. "Convolution Neural Network Approach for Single Image Super Resolution," International Journal of Scientific Research in Computer Science, Engineering and Information Technology, International Journal of Scientific Research in Computer Science, Engineering and Information Technology, vol. 10(6), pages 2403-2408, November.
    • Handle: RePEc:jbh:ijsrcs:v10:y2024:i6:id:643
    DOI: 10.32628/CSEIT2410492
    Note: Article URL: https://ijsrcseit.com/home/article/view/CSEIT2410492
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