Neural Image Blending for Temporal Upscaling Stability
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Solution Overview
Problem
Generating high-quality image and video content at higher resolutions is resource-intensive, and determining optimal blending weights for temporal smoothing between frames is challenging, often leading to noisy or artifact-prone images with issues like ghosting and temporal instability.
Innovation Solution
A deep learning-based approach that utilizes neural networks to predict blending factors and incorporate spatial and temporal gradients, along with motion vectors and depth information, to enhance image upscaling and reduce artifacts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If temporal blending is used to improve image quality, then temporal smoothing and pixel data accumulation are achieved, but determining optimal blending weights becomes challenging leading to artifacts
Solution Approach 1:
The patent replaces manual or heuristic blending weight determination with an automated neural network system. The neural network learns optimal blending weights through training on image data, substituting complex manual tuning processes with an automated machine learning approach that directly predicts weights based on input frame characteristics.
Solution Approach 2:
The neural network performs self-learning and self-optimization by training on image sequences to automatically determine optimal blending weights. The system serves itself by learning from data what weights produce the best temporal smoothing results, eliminating the need for external manual intervention in weight determination.
2Productivity
If improper blending weights are used, then processing resources are reduced, but image quality deteriorates with noise and artifacts
Solution Approach 1:
The neural network is trained in advance on large datasets of image sequences to learn optimal blending strategies before actual use. This preliminary training phase allows the network to internalize patterns and relationships, so that during real-time operation it can quickly and accurately determine weights without requiring extensive computation or trial-and-error adjustments.
Solution Approach 2:
The system dynamically adjusts blending weights based on learned parameters from the neural network. Instead of using fixed or manually set weights, the system changes weights adaptively based on the specific characteristics of each frame sequence, allowing optimization of both quality and processing efficiency for different content types.
3Manufacturing precision
If high resolution image generation is implemented, then content quality is improved, but resource consumption increases significantly
Solution Approach 1:
The system applies temporal blending selectively based on motion detection and scene analysis. Instead of uniformly processing all pixels at full resolution with equal blending, the neural network identifies regions that benefit most from temporal accumulation and applies blending weights selectively, performing partial processing on only the necessary portions of the image data.
Solution Approach 2:
The image processing is segmented into different regions with different blending strategies. The neural network divides the image into areas with varying motion characteristics and applies appropriate blending weights to each segment, allowing high-quality reconstruction where needed while reducing processing in areas where temporal blending is less beneficial.
Data Source
AI summary
Apparatuses, systems, and techniques are presented to reconstruct one or more images. In at least one embodiment, one or more neural networks are used to determine one or more second colors of one or more pixels based, at least in part, on one or more spatial variations and/or temporal variations of a first color of the one or more pixels.