2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information

2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information
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Paper Detail

Paper IDIVMSP-6.2
Paper Title JOINT COUPLED TRANSFORM LEARNING FRAMEWORK FOR MULTIMODAL IMAGE SUPER-RESOLUTION
Authors Andrew Gigie, Achanna Anil Kumar, TCS Research and Innovation, India; Angshul Majumdar, IIIT Delhi, India; Kriti Kumar, M Girish Chandra, TCS Research and Innovation, India
SessionIVMSP-6: Super-resolution 2 & Multi-scale Processing
LocationGather.Town
Session Time:Tuesday, 08 June, 16:30 - 17:15
Presentation Time:Tuesday, 08 June, 16:30 - 17:15
Presentation Poster
Topic Image, Video, and Multidimensional Signal Processing: [IVTEC] Image & Video Processing Techniques
IEEE Xplore Open Preview  Click here to view in IEEE Xplore
Abstract Insights from multiple imaging modalities have recently been applied in solving many computer vision related applications. In this paper, we model the cross-modal dependencies between different modalities for Multimodal Image Super-Resolution (MISR), i.e., enhance the Low Resolution (LR) image of target modality with the guidance of a High Resolution (HR) image from another modality. We introduce a joint optimization based transform learning framework referred to as Joint Coupled Transform Learning (JCTL) to combine the information from multiple modalities to generate the HR image of the target modality. All the necessary intermediate steps and the corresponding closed form solution updates are provided. The performance of the proposed JCTL is benchmarked against the state-of-the-art MISR approaches on different multimodal datasets with different upscaling factors. The results show better performance with the proposed JCTL approach compared to other state-of-the-art techniques both in terms of PSNR and SSIM.