Deep Learning Focus Map Generation for All-in-Focus Imaging
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional methods for generating all-in-focus images from multi-focus images rely on calculating focus maps based on image stacks, which can be inaccurate and labor-intensive, especially when dealing with varying depths and focuses.
Innovation Solution
The use of deep learning, specifically convolutional neural networks, to generate focus maps and create all-in-focus images by calculating weights proportional to focus values across multiple images, thereby improving image clarity and accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional methods calculate focus maps based on image stacks, then all-in-focus images can be generated, but the process is inaccurate and labor-intensive
Solution Approach 1:
The patent replaces conventional mechanical/image-processing methods with deep learning-based neural networks. The system uses a trained neural network model to automatically calculate focus maps from multi-focus images, substituting manual or algorithmic focus detection with learned patterns from training data, thereby improving both accuracy and efficiency
Solution Approach 2:
The patent performs preliminary training of the neural network model using labeled training data containing multi-focus images and their corresponding focus maps. This pre-computed knowledge is stored in the model and applied during inference, eliminating the need to recalculate focus maps from scratch for each new image set
2Measurement precision
If deep learning is used to generate focus maps, then accuracy and efficiency improve, but computational complexity increases
Solution Approach 1:
The patent uses a pre-trained neural network model that has been copied from training phase to inference phase. The model weights and parameters are replicated and applied to new images without retraining, simplifying the deployment process while maintaining high accuracy
Solution Approach 2:
The neural network model is designed to be universal, handling various multi-focus images with different depths, focuses, and lighting conditions. The single model performs multiple functions: focus detection, focus map generation, and image fusion guidance, eliminating the need for separate processing pipelines
3Reliability
If multiple multi-focus images are captured at the same position, then all-in-focus images can be generated, but the process becomes labor-intensive
Solution Approach 1:
The system automatically processes multiple multi-focus images through the neural network model without requiring manual intervention. The model self-determines focus maps, self-weights images based on focus quality, and self-fuses them into all-in-focus images, eliminating manual labor while ensuring high clarity
Solution Approach 2:
The patent implements a feedback mechanism where the neural network model evaluates the quality of focus in each input image and adjusts weighting accordingly. The model receives feedback from the multi-focus image set and iteratively optimizes the all-in-focus image generation, ensuring high reliability while automating the process
Data Source
AI summary
Disclosed herein is a method for operating an apparatus for generating an all-in-focus image. The method may include generating a focus-map-calculating model through deep learning for images having different focuses and a reference all-in-focus image corresponding thereto, calculating a focus map for each of multi-focus images, which are captured by a camera, using the focus-map-calculating model, and generating an all-in-focus image for the multi-focus images using the focus map.