Implicit Neural Representation for Video and Image Super-Resolution

• URL: http://arxiv.org/abs/2503.04665v1
• Date: Thu, 06 Mar 2025 17:58:55 GMT
• Title: Implicit Neural Representation for Video and Image Super-Resolution
• Authors: Mary Aiyetigbo, Wanqi Yuan, Feng Luo, Nianyi Li,
• Abstract summary: We present a novel approach for super-resolution that utilizes implicit neural representation (INR)<n>Our method facilitates high-resolution reconstruction using only low-resolution inputs and a 3D high-resolution grid.<n>Our proposed method, SR-INR, maintains consistent details across frames and images, achieving impressive temporal stability.
• Score: 4.960738913876514
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We present a novel approach for super-resolution that utilizes implicit neural representation (INR) to effectively reconstruct and enhance low-resolution videos and images. By leveraging the capacity of neural networks to implicitly encode spatial and temporal features, our method facilitates high-resolution reconstruction using only low-resolution inputs and a 3D high-resolution grid. This results in an efficient solution for both image and video super-resolution. Our proposed method, SR-INR, maintains consistent details across frames and images, achieving impressive temporal stability without relying on the computationally intensive optical flow or motion estimation typically used in other video super-resolution techniques. The simplicity of our approach contrasts with the complexity of many existing methods, making it both effective and efficient. Experimental evaluations show that SR-INR delivers results on par with or superior to state-of-the-art super-resolution methods, while maintaining a more straightforward structure and reduced computational demands. These findings highlight the potential of implicit neural representations as a powerful tool for reconstructing high-quality, temporally consistent video and image signals from low-resolution data.

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