Gaussian Patch Blending for UV Image Synthesis
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Solution Overview
Problem
Current image-to-image translation methods face challenges in synthesizing high-quality ultraviolet (UV) images from RGB images, particularly due to gridding and color inconsistency issues, especially when translating skin images, which are essential for skin condition analysis and simulation.
Innovation Solution
A novel Gaussian-based patch blending technique is employed within a cycle-consistent Generative Adversarial Network (GAN) framework to weigh overlapping pixels based on their distance to the patch center, ensuring smooth and accurate translation between RGB and UV domains, thereby addressing gridding and color inconsistencies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If conventional image-to-image translation methods are used to synthesize UV images from RGB images, then the translation process can be performed, but gridding and color inconsistency issues occur, reducing image quality
Solution Approach 1:
The input image is divided into overlapping patches that are processed independently through the generator, then blended together. This segmentation allows the generator to focus on local features and reduce global inconsistencies, while the overlapping regions enable smooth transitions and reduce gridding artifacts.
Solution Approach 2:
Different regions of the image are processed with different patch sizes and overlap configurations. The blending weights are dynamically adjusted based on the local content and characteristics, allowing optimal translation quality for each region while maintaining overall consistency.
2Manufacturing precision
If patch blending is used to construct output images from overlapping patches, then translation accuracy improves, but computational time and processing complexity increase
Solution Approach 1:
Instead of processing the entire image as a single patch, the method uses multiple overlapping patches with strategic overlap. This partial action approach maintains high translation accuracy through localized processing while reducing overall computational complexity compared to single large-patch processing.
Solution Approach 2:
The patch size, overlap amount, and blending weights are optimized parameters that can be adjusted based on image resolution and content. By tuning these parameters, the system achieves high accuracy with reduced processing time, balancing quality and efficiency.
3Manufacturing precision
If Gaussian-based patch blending is applied to weigh overlapping pixels, then blending smoothness and accuracy improve, but computational complexity increases
Solution Approach 1:
The Gaussian blending function is applied consistently across all overlapping patches, using the same mathematical formulation and weighting approach. This standardized copying of the blending process simplifies implementation while maintaining high accuracy through the well-understood properties of Gaussian functions.
Data Source
AI summary
A method, apparatus and system according to embodiments provide image-to-image translations such as synthesis of ultraviolet (UV) images from input images in a RGB (red, green blue) color model. In an embodiment, a trained generator generates overlapping UV patch images from overlapping RBG patch images extracted from an input image. The overlapping UV patch images are blended using a Gaussian weighting factor applied to overlapping pixels having a same location in the input image. The Gaussian blending distributes weights to pixels relative to the pixel's distance to the center of its patch, with weighting being highest at the center.