Neural Frame Interpolation Using Event-Guided Feature Fusion
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Solution Overview
Problem
Existing technologies face challenges in efficiently generating high-quality and high-definition images by effectively interpolating frames using neural networks, particularly in generating accurate intermediate frames between two input frames.
Innovation Solution
A frame interpolation system utilizing a neural network that processes multiple pieces of frame and event data to generate high-quality images by encoding, fusing feature data, generating weight maps, and blending these features to produce accurate interpolation frames.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If frame interpolation is performed using existing neural network methods, then intermediate frames can be generated, but image quality deteriorates with video shaking and blur
Solution Approach 1:
The system segments the frame interpolation task into multiple independent processing streams: one stream processes image data through encoders to extract visual features, while another stream processes event data through separate encoders to capture motion information. These segmented streams are then fused at the feature level to produce high-quality interpolated frames, with each segment handling specific aspects of the problem independently.
Solution Approach 2:
Event data serves as an intermediary element that bridges the gap between consecutive image frames. The event data, which captures instantaneous changes in the visual scene, acts as a mediator that provides precise motion information to guide the interpolation process, thereby reducing blur and shaking in the generated intermediate frames.
Solution Approach 3:
The system combines multiple data types (image data and event data) and multiple feature representations into a composite feature set. This composite approach integrates the spatial information from images with the temporal-m motion information from events, creating a rich composite representation that enables high-quality frame interpolation while minimizing artifacts.
2Manufacturing precision
If multiple encoders and feature fusion modules are used to improve image quality, then frame interpolation accuracy improves, but system complexity increases
Solution Approach 1:
The complex system is segmented into modular components: separate encoder modules for image and event data, distinct feature fusion modules, and independent processing streams. This segmentation allows each module to be optimized independently while maintaining overall system coherence, managing complexity through structured modularity.
Solution Approach 2:
The encoder modules and feature fusion modules are designed with multi-functionality to handle different types of input data (images and events) and produce unified feature representations. This universal design reduces the need for separate specialized components, thereby managing system complexity while maintaining high interpolation quality.
Data Source
AI summary
Provided are a frame interpolation system, an operating method of a frame interpolation system, and an image processing device including the same. The frame interpolation system according to one or more embodiments may include a first plurality of encoders, a second plurality of encoders, a third encoder, a first feature fusion module, a second feature fusion module, a weight map generation module, a blending module, and a decoder.