Neural Network Image Blending for Temporal Upsampling
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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-ridden images with ghosting or temporal instability.
Innovation Solution
A deep learning-based system for temporal upsampling that uses a neural network to predict blending factors and apply filtering, warping historical frame data based on motion vectors to achieve high-resolution images with reduced noise and artifacts, leveraging a refinement network for improved pixel-specific weightings and anti-aliasing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If blending of current and prior frame data is used to improve quality, then temporal smoothing and pixel data accumulation are achieved, but determining optimal blending weights becomes challenging and can produce noisy images or artifacts
Solution Approach 1:
A neural network is introduced as an intermediary component that automatically learns and determines optimal blending weights from input frames. The neural network processes current and prior frame data, motion vectors, and other features to generate pixel-specific blending weights, eliminating the need for manual weight determination and avoiding artifacts while achieving temporal smoothing.
Solution Approach 2:
The system dynamically adjusts blending weights on a per-pixel basis rather than using fixed or uniform weights. The neural network generates different weight values for different pixels based on local image characteristics, motion information, and temporal coherence, allowing optimal blending that adapts to varying content requirements across the image.
2Manufacturing precision
If higher resolution content is generated to improve quality, then display quality increases, but resource intensity increases significantly for devices with limited capacity
Solution Approach 1:
The system performs preliminary processing by generating intermediate representations and features from input frames before final reconstruction. Motion vectors, depth information, and other predictive features are computed in advance to guide the blending and upsampling processes, reducing the computational burden during real-time high-resolution generation.
Solution Approach 2:
The system leverages the temporal dimension by incorporating multiple prior frames and motion information to reconstruct current high-resolution frames. Instead of relying solely on spatial information from a single frame, the system uses temporal coherence and motion-compensated blending across multiple time steps to achieve high resolution with reduced computational requirements.
3Productivity
If traditional upsampling methods are used to increase resolution, then processing speed is maintained, but image quality suffers from noise and artifacts
Solution Approach 1:
Traditional mechanical upsampling algorithms are replaced with a neural network-based system that learns optimal blending strategies from data. The neural network substitutes conventional interpolation and filtering methods with learned patterns that better preserve image quality while maintaining real-time processing capabilities through optimized inference.
Solution Approach 2:
The neural network is trained to automatically determine optimal processing parameters and blending weights without manual intervention or post-processing correction. The system self-adjusts to different content types, motion levels, and resolution requirements, achieving both high quality and efficient processing through adaptive, content-aware decision-making.
Data Source
AI summary
Apparatuses, systems, and techniques are presented to reconstruct one or more images. In at least one embodiment, one or more circuits are to use one or more neural networks to adjust one or more pixel blending weights.