FMA-Net enables precise estimation of degradation and restoration kernels, handles large motions, and refines features in a course-to-fine manner through iterative updates.
Enables precise estimation of spatio-temporally-variant degradation and restoration kernels through sophisticated motion representation learning.
Refines features in a course-to-fine manner through iterative updates, leveraging multi-attention to anchor and sharpen features temporally.
Temporally anchors and sharpens features, enabling effective feature refinement and restoration.
Trained with the novel temporal anchor loss, these blocks enable iterative feature refinement and restoration.
The FMA-Net architecture consists of the flow-guided dynamic filtering and iterative feature refinement with multi-attention blocks, enabling joint video super-resolution and deblurring.
Restore clean high-resolution videos from blurry low-resolution ones.
Enhance video quality for surveillance, entertainment, and healthcare applications.
Apply FMA-Net to various video processing tasks, such as video super-resolution, deblurring, and enhancement.
Train the FMA-Net model using the provided training dataset and code.
Fine-tune the model for specific video processing tasks or applications.
Apply the trained model to various video processing tasks, such as video super-resolution, deblurring, and enhancement.