Temporal Image Blending Neural Network Super-Resolution
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Solution Overview
Problem
Current methods for generating high-quality image and video content at higher resolutions are resource-intensive, particularly for devices with limited capacity, and struggle with determining optimal blending weights for temporal smoothing, often resulting in noisy or artifact-prone images with ghosting or temporal instability.
Innovation Solution
A deep learning-based super-resolution process that upscales images by using motion vector data to align and blend frames, incorporating jitter-aware resampling and dilation to maintain fine detail and reduce bandwidth usage, while a neural network determines blending factors to achieve smooth animation and reduce artifacts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If deep learning-based super-resolution with temporal blending is used, then image quality and temporal stability are improved, but computational resource requirements increase
Solution Approach 1:
The system segments the computational task by separating the neural network processing from the rendering pipeline. The neural network is trained offline to learn optimal blending weights, and during runtime, only the pre-computed weights and simple blending operations are applied, dividing the heavy computational burden from the real-time processing requirements.
Solution Approach 2:
The neural network that determines optimal blending weights is trained in advance during an offline phase. This preliminary training allows the system to store pre-computed knowledge about optimal blending strategies, so that during actual rendering, only lightweight inference and blending operations are needed, significantly reducing real-time computational requirements.
2Use of energy by moving object
If temporal blending with improper weights is used, then resource usage is reduced, but image quality deteriorates with noise and artifacts
Solution Approach 1:
The system incorporates feedback mechanisms where the neural network learns from training data the optimal blending weights that minimize artifacts and noise. The network analyzes patterns in frame sequences and adjusts weights dynamically based on motion characteristics, providing adaptive feedback that maintains high image quality while optimizing resource usage through intelligent blending decisions.
Solution Approach 2:
The system dynamically changes the blending weight parameter based on scene characteristics, motion vectors, and temporal coherence measurements. By adapting the blending weight from fixed to dynamic and context-dependent, the system achieves high image quality across varying conditions while optimizing resource allocation through selective application of intensive processing only when needed.
3Manufacturing precision
If high resolution rendering is used, then image quality is improved, but processing speed decreases
Solution Approach 1:
The system uses temporal copying by reusing and blending information from previously rendered frames. Instead of rendering every frame at full resolution independently, the system copies and adapts high-quality features from prior frames, combining them with current frame data through neural network-guided blending. This approach achieves high-resolution quality at lower processing speeds by leveraging temporal redundancy.
Solution Approach 2:
The system implements dynamic resolution blending where the effective resolution and blending intensity adjust based on scene complexity, motion magnitude, and importance of temporal coherence. In static or low-motion regions, higher blending weights are applied to reuse prior frame data, while in high-motion or complex regions, more current frame rendering is performed, creating a dynamic balance between quality and processing speed.
Data Source
AI summary
Apparatuses, systems, and techniques are presented to reconstruct one or more images. In at least one embodiment, one or more objects in an image are caused to be generated based, at least in part, on applying one or more offsets to a motion of the one or more objects relative to one or more prior images.