UDC Image Restoration With Adaptive Noise and Blur Tuning
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Solution Overview
Problem
Under-display cameras (UDCs) capture images with degradation factors such as noise and blur due to light passing through display pixels, which existing image restoration methods fail to adequately address, leading to suboptimal image quality.
Innovation Solution
An image restoration method and apparatus using a deep neural network that determines degradation information, including noise and blur parameters, and tunes these parameters based on environmental conditions and user preferences to generate a restored image.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If existing image restoration methods are used on UDC images, then processing speed is maintained, but image quality remains suboptimal due to inadequate handling of noise and blur
Solution Approach 1:
The image restoration process is segmented into distinct stages: degradation information determination, parameter tuning based on environmental conditions and user preferences, and restored image generation. This segmentation allows each component to be optimized independently, improving overall image quality without proportionally increasing system complexity
Solution Approach 2:
The system dynamically adjusts restoration parameters based on determined degradation information and environmental conditions. By changing parameters adaptively rather than using fixed restoration settings, the system achieves superior image quality across varying UDC degradation scenarios
2Measurement precision
If degradation information is determined and tuned for each UDC image, then restoration accuracy improves, but processing time increases
Solution Approach 1:
Degradation information is determined and parameters are tuned before the actual image restoration process. This preliminary analysis allows the restoration algorithm to be pre-configured with optimal parameters, improving restoration accuracy while minimizing the time required during the main processing phase
Solution Approach 2:
The system uses determined degradation information as feedback to adjust restoration parameters dynamically. This feedback mechanism ensures high restoration accuracy by adapting to actual image conditions without requiring excessive processing time, as the feedback loop is integrated efficiently into the restoration pipeline
3Adaptability or versatility
If noise and blur parameters are tuned based on environmental conditions, then restoration performance across varying conditions improves, but system complexity increases
Solution Approach 1:
The parameter tuning system is designed to handle multiple degradation conditions (noise, blur, illumination variations) using a unified framework. This universal approach allows the system to adapt to various environmental conditions without requiring separate specialized modules for each condition, thereby limiting the increase in system complexity
Data Source
AI summary
An image restoration method includes determining degradation information indicating a degradation factor of a degraded image, tuning the degradation information based on a tuning condition, and generating a restored image corresponding to the degraded image by executing an image restoration network with the degraded image and the degradation information.