Improving Image Stitching Effect using Super-Resolution Technique

This paper aims to present a novel methodology that merges image stitching with super-resolution techniques, enabling the creation of a high-resolution panoramic image from several low-resolution inputs. The proposed approach comprehensively addresses challenges throughout the process, encompassing image preprocessing, alignment and handling of mismatches, stitching, super-resolution reconstruction, and post-processing. Employing advanced methodologies such as Convolutional Neural Networks (CNNs), Scale-Invariant Feature Transform (SIFT), Random Sample Consensus (RANSAC), GrabCut algorithm, Super-Resolution Convolutional Neural Network (SRCNN), gradient domain optimization, and Structural Similarity Index Measure (SSIM), each step meticulously tackles specific issues inherent to image stitching tasks. A key innovation lies in the synergy of image stitching and super-resolution techniques, yielding a solution that boasts high robustness and efficiency. This versatile method is adaptable to diverse image processing contexts. To validate its effectiveness, experiments were conducted on two established datasets, USIS-D and VGG, where a quartet of quantitative metrics – Peak Signal-to-Noise Ratio (PSNR), SSIM, Entropy (EN), and Quality Assessment of Blurred Faces (QABF) – were employed to gauge the quality of stitched images against alternative methods. The outcomes decisively illustrate the superiority of our proposed method, achieving superior performance across all metrics and producing panoramas devoid of seams and distortions. This work thereby contributes a significant advancement in the realm of high-fidelity panoramic image reconstruction.

@article{Liu2024,
  title     = {Improving Image Stitching Effect using Super-Resolution Technique},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {15},
  number    = {6},
  year      = {2024},
  publisher = {The Science and Information Organization},
  author    = {Jinjun Liu},
  doi       = {10.14569/IJACSA.2024.0150693},
  url       = {https://doi.org/10.14569/IJACSA.2024.0150693}
}