Open Access Open Access  Restricted Access Subscription Access
Open Access Open Access Open Access  Restricted Access Restricted Access Subscription Access

Enhancing Image Super-Resolution With Deep Convolutional Neural Networks


Affiliations
1 Department of Computer Science, Mandsaur University, India
     

   Subscribe/Renew Journal


In computer vision, image super-resolution plays a pivotal role in improving the visual quality of low-resolution images, thereby enhancing various applications such as medical imaging, surveillance, and digital entertainment. The problem at hand involves the inherent limitations of conventional methods in restoring high-frequency information lost during image downscaling. This research aims to bridge this gap by leveraging DCNNs, exploiting their ability to learn complex mappings between low and high-resolution image spaces. This study addresses the challenge of image super-resolution through the application of Deep Convolutional Neural Networks (DCNNs). The research involves the design and training of a novel DCNN architecture tailored specifically for image super-resolution. We employ a large dataset of low and high-resolution image pairs to facilitate supervised learning. The network is trained to intelligently infer high-frequency details from low-resolution inputs, enabling the generation of visually compelling super-resolved images. Results from extensive experiments showcase the superior performance of the proposed DCNN-based approach compared to traditional methods. Quantitative metrics, such as peak signal-to-noise ratio (PSNR) and structural similarity index (SSI), demonstrate significant improvements in image quality. Additionally, qualitative assessments highlight the network’s ability to reconstruct fine details, edges, and textures, resulting in visually pleasing super-resolved images.

Keywords

Deep Convolutional Neural Networks, Image Super-Resolution, Computer Vision, Neural Network Training, High-Resolution Imaging
Subscription Login to verify subscription
User
Notifications
Font Size

  • R. Tenne and D. Oron, “Super-Resolution Enhancement by Quantum Image Scanning Microscopy”, Nature Photonics, Vol. 13, No. 2, pp. 116-122, 2019.
  • C. Zhao and J.L. Prince, “Applications of a Deep Learning Method for Anti-Aliasing and Super-Resolution in MRI”, Magnetic Resonance Imaging, Vol. 64, pp. 132-141, 2019.
  • X. Hu, M. Lamm and P. Fieguth, “RUNet: A Robust UNet Architecture for Image Super-Resolution”, Proceedings of IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 1-5, 2019.
  • K. Stengel and R.N. King, “Adversarial Super-Resolution of Climatological Wind and Solar Data”, Proceedings of the National Academy of Sciences, Vol. 117, No. 29, pp. 16805-16815, 2020.
  • Y. Da Wang, R.T. Armstrong and P. Mostaghimi, “Enhancing Resolution of Digital Rock Images with Super Resolution Convolutional Neural Networks”, Journal of Petroleum Science and Engineering, Vol. 182, pp. 106261-106271, 2019.
  • K. Yamashita and K. Markov, “Medical Image Enhancement using Super Resolution Methods”, Proceedings of International Conference on Computational Science, pp. 496-508, 2020.
  • D. Kim and T.H. Kim, “Depth-Controllable very Deep Super-Resolution Network”, Proceedings of International Conference on Neural Networks, pp. 1-8, 2019.
  • N. Majidi and R. Rastgoo, “A Deep Model for Super-Resolution Enhancement from a Single Image”, Journal of AI and Data Mining, Vol. 8, No. 4, pp. 451-460, 2020.
  • N.C. Rakotonirina and A. Rasoanaivo, “ESRGAN+: Further Improving Enhanced Super-Resolution Generative Adversarial Network”, Proceedings of International Conference on Acoustics, Speech and Signal Processing, pp. 3637-3641, 2020.
  • J. Chen, J. Liu and H. Shroff, “Three-Dimensional Residual Channel Attention Networks Denoise and Sharpen Fluorescence Microscopy Image”, Nature Methods, Vol. 18, No. 6, pp. 678-687, 2021.
  • R. Lan and X. Luo, “Cascading and Enhanced Residual Networks for Accurate Single-Image Super-Resolution”, IEEE Transactions on Cybernetics, Vol. 51, No. 1, pp. 115-125, 2020.
  • L. Gong, Y. Ma and Z. Huang, “Higher-Order Coherent Anti-Stokes Raman Scattering Microscopy Realizes Label-Free Super-Resolution Vibrational Imaging”, Nature Photonics, Vol. 14, No. 2, pp. 115-122, 2020

Abstract Views: 162

PDF Views: 1




  • Enhancing Image Super-Resolution With Deep Convolutional Neural Networks

Abstract Views: 162  |  PDF Views: 1

Authors

Archana Tomar
Department of Computer Science, Mandsaur University, India
Harish Patidar
Department of Computer Science, Mandsaur University, India

Abstract


In computer vision, image super-resolution plays a pivotal role in improving the visual quality of low-resolution images, thereby enhancing various applications such as medical imaging, surveillance, and digital entertainment. The problem at hand involves the inherent limitations of conventional methods in restoring high-frequency information lost during image downscaling. This research aims to bridge this gap by leveraging DCNNs, exploiting their ability to learn complex mappings between low and high-resolution image spaces. This study addresses the challenge of image super-resolution through the application of Deep Convolutional Neural Networks (DCNNs). The research involves the design and training of a novel DCNN architecture tailored specifically for image super-resolution. We employ a large dataset of low and high-resolution image pairs to facilitate supervised learning. The network is trained to intelligently infer high-frequency details from low-resolution inputs, enabling the generation of visually compelling super-resolved images. Results from extensive experiments showcase the superior performance of the proposed DCNN-based approach compared to traditional methods. Quantitative metrics, such as peak signal-to-noise ratio (PSNR) and structural similarity index (SSI), demonstrate significant improvements in image quality. Additionally, qualitative assessments highlight the network’s ability to reconstruct fine details, edges, and textures, resulting in visually pleasing super-resolved images.

Keywords


Deep Convolutional Neural Networks, Image Super-Resolution, Computer Vision, Neural Network Training, High-Resolution Imaging

References