Single-Image Light Field Correction Using a Trained Image Model
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing image processing methods require multiple images to correct light field variations, leading to increased costs and resource consumption due to inefficient light field correction processes.
Innovation Solution
An image processing method and apparatus that trains an image processing model using a standard image and light field variation parameters to predict and correct light fields in a single image, utilizing a correction network trained with a sample set of different light field backgrounds.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple images are collected repeatedly for light field correction, then correction accuracy is improved, but photographing costs and image storage costs increase
Solution Approach 1:
The patent extracts the light field correction problem from the multi-image processing workflow and creates a dedicated light field correction network that operates on single images. This separates the correction function from the image collection process, eliminating the need to store multiple images while maintaining correction accuracy through the specialized network architecture.
Solution Approach 2:
The patent creates a virtual copy of the light field correction process through a neural network model that simulates the effect of multiple image processing. The trained network reproduces correction results equivalent to multi-image processing without requiring actual multiple images, thus reducing storage requirements while preserving correction quality.
2Measurement precision
If multiple images are collected repeatedly for light field correction, then correction accuracy is improved, but photographing time increases
Solution Approach 1:
The patent performs preliminary training of the light field correction network using diverse training images that represent various lighting conditions and scenarios. This pre-training phase prepares the network to handle different situations without requiring multiple actual photographs during operation, thus reducing correction time while maintaining accuracy across different conditions.
Solution Approach 2:
The patent replaces the mechanical process of collecting multiple physical images with a computational neural network processing system. The network processes single images through learned patterns and transformations, substituting the physical act of repeated photographing with efficient computational operations that achieve the same correction goal faster.
3Reliability
If traditional light field correction methods are used, then correction completeness is improved, but computing resources are wasted
Solution Approach 1:
The patent transforms the correction approach by changing parameters from processing multiple images in traditional algorithms to processing single images through a trained neural network. The network learns optimal correction parameters during training and applies them efficiently during inference, maintaining correction completeness while reducing computational overhead through optimized network architecture and operations.
Solution Approach 2:
The patent performs comprehensive correction learning during the offline training phase, where the network learns to handle various lighting conditions and correction scenarios. This preliminary action embeds correction knowledge into the network weights, enabling efficient online correction without requiring heavy computational resources during actual operation, thus preserving completeness while reducing runtime resource consumption.
Data Source
AI summary
An image processing method includes: acquiring a standard image matched with an image processing model; determining light field variation parameters of a use environment of the image processing model based on the standard image; determining an image training sample set based on the light field variation parameters, the image training sample set including images of different light field backgrounds; training a correction network of the image processing model through the image training sample set to obtain a model updating parameter adapted to the correction network, and generating a trained image processing model based on the model updating parameter; acquiring a single image in the use environment, and performing prediction processing on the single image through the trained image processing model to obtain light field information corresponding to the single image; and correcting a light field of the single image based on the light field information.