arXiv:2512.04390v2 Announce Type: replace-cross Abstract: Joint video super-resolution and deblurring (VSRDB) requires both efficient long-range temporal modeling and robustness to frame-wise exposure-duration variation, which changes the extent of motion blur across video frames. We propose FMA-Net++, a non-recurrent, sequence-level framework built from Hierarchical Refinement with Bidirectional Aggregation (HRBA) blocks. By stacking HRBA blocks, FMA-Net++ processes video frames in parallel while hierarchically expanding the temporal receptive field, avoiding the limited temporal receptive fi
The continuous advancements in AI research, particularly in computer vision, are driving increasingly sophisticated solutions for video processing challenges like super-resolution and deblurring.
Improved video super-resolution and deblurring can significantly enhance the quality of visual data for various applications, including surveillance, autonomous systems, media production, and AI training datasets.
This research introduces a novel non-recurrent framework that addresses the complex issues of temporal modeling and exposure variation in joint video super-resolution and deblurring, potentially setting a new standard for performance in difficult imaging conditions.
- · Computer Vision Researchers
- · Surveillance Technology Companies
- · Autonomous Vehicle Developers
- · Media Production Studios
- · Traditional Video Processing Solutions
- · Hardware-only Upscaling Solutions
Higher quality and more reliable video data will become available for a wider range of applications.
This could accelerate the development and deployment of AI systems reliant on complex visual input, such as those in robotics and real-time analytics.
The integration of such sophisticated video enhancement techniques might open new markets for AI-powered visual data services and products.
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Read at arXiv cs.AI