Burst Super-Resolution Training Without High-Resolution Ground Truth
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Solution Overview
Problem
Conventional image capturing systems face inefficiencies and inaccuracies in generating high-quality images due to the failure to model noise distribution, blur kernels, camera trajectory, and object motion in real-world scenarios, leading to poor performance in real-world applications.
Innovation Solution
A self-supervised training approach for burst super-resolution that utilizes low-resolution image bursts to train image burst models, accounting for dynamic motion and noise distribution, and generates high-resolution images by simulating real-world degradation processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional supervised training methods using high-resolution ground truth images are employed, then training accuracy can be maintained, but data collection becomes computationally inefficient and inflexible
Solution Approach 1:
The system performs self-supervised learning by automatically generating supervision signals from the input low-resolution image bursts themselves, without requiring external high-resolution ground truth data. The training process serves itself by creating synthetic high-resolution images through the image formation model and using these to supervise the learning, thereby eliminating the need for separate data collection pipelines.
Solution Approach 2:
The system pre-trains the image formation model to simulate real-world degradation processes before using it to generate supervision signals. This preliminary modeling of noise distribution, blur kernels, and camera trajectory allows the subsequent self-supervised training to proceed without requiring actual high-resolution reference images.
2Manufacturing precision
If real-world degradation processes are accurately modeled, then image generation quality improves, but system complexity increases
Solution Approach 1:
The system segments the complex image formation process into distinct learnable components: noise distribution modeling, blur kernel estimation, camera trajectory prediction, and object motion compensation. Each component is handled by a separate module within the image formation model, making the overall complex system more manageable and trainable through self-supervision.
Solution Approach 2:
The system dynamically adjusts parameters of the image formation model during self-supervised training, including noise levels, blur kernel sizes, and motion parameters, to match the specific characteristics of the input image burst. This adaptive parameter tuning allows accurate modeling of real-world degradation without requiring a fixed complex model structure.
3Measurement precision
If high-resolution ground truth images are collected for training, then supervision accuracy is maintained, but the process becomes computationally inefficient and inaccurate for real-world scenarios
Solution Approach 1:
Instead of using actual high-resolution ground truth images, the system creates synthetic copies of what the high-resolution images would look like by processing the low-resolution input through the learned image formation model. These synthetic high-resolution images serve as supervision targets, providing accurate guidance without requiring physical high-resolution reference images.
Solution Approach 2:
The image formation model acts as an intermediary that bridges the gap between low-resolution input images and the hypothetical high-resolution ground truth. It transforms the low-resolution input according to learned degradation patterns to generate intermediate supervision signals that guide the super-resolution learning process.
4Adaptability or versatility
If the system adapts to various image capturing devices, then versatility improves, but training data requirements become more stringent
Solution Approach 1:
The image formation model is designed with universal components that can model different types of image degradation across various camera devices. By learning device-specific parameters during self-supervised training on device-specific data, the same model architecture can be applied to smartphones, DSLRs, and other imaging devices without requiring separate training pipelines.
Data Source
AI summary
The present disclosure relates to systems, methods, and non-transitory computer-readable media that implements self-supervised training of an image burst model, trained exclusively on low-resolution images. For example, the disclosed system accesses an image burst that includes a plurality of images. The disclosed system generates a high-resolution image estimation from a first subset of images of the plurality of images. Further, the disclosed system generates a set of low-resolution images by modifying the high-resolution image estimation based on parameters of one or more images from the plurality of images. Moreover, the disclosed system determines a measure of loss by comparing the set of low-resolution images with a second subset of images from the plurality of images and updates the image burst model with the determined measure of loss.