AI Imaging System for Lens Noise Classification and Personalized Correction
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing imaging devices struggle with lens contamination due to environmental factors like fog, dust, and droplets, leading to unwanted artifacts, improper noise correction, and reduced image quality, with current solutions failing to differentiate between scene and lens contamination and often applying uniform correction methods, resulting in blurred or low-light images.
Innovation Solution
A method and system using AI techniques to classify lens noise, preprocess images with noise filters, and utilize user feedback to dynamically adjust regions of interest and hardware parameters, employing deep learning networks for personalized noise correction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If state-of-the-art intelligent scene understanding techniques are used to correct lens contamination, then image correction capability is improved, but system performance slows down due to dependency on server and inability to handle different noise types effectively
Solution Approach 1:
The patent segments the image processing task by dividing the contaminated image into multiple regions of interest (ROIs) based on noise detection. Different processing strategies are applied to different regions: severe contamination areas receive aggressive denoising while less affected areas maintain higher fidelity. This segmentation allows the system to balance correction effectiveness with processing speed by not uniformly applying computationally intensive operations across the entire image.
Solution Approach 2:
The patent applies partial action by selectively processing only the contaminated regions identified through noise detection rather than the entire image. The system determines a threshold for contamination severity and applies correction operations only to pixels or regions exceeding this threshold, thereby reducing overall computational load while maintaining correction effectiveness in affected areas.
2Device complexity
If uniform noise correction techniques are applied to all types of contamination, then processing simplicity is maintained, but correction effectiveness deteriorates due to inability to differentiate between scene and lens contamination
Solution Approach 1:
The patent implements local quality by analyzing the spatial distribution and characteristics of noise in different regions of the image. The system identifies whether contamination patterns are consistent with lens contamination (e.g., radial patterns from droplets, uniform dust particles) versus scene contamination (e.g., fog, atmospheric haze). Based on this local analysis, different correction parameters and algorithms are applied to different regions, achieving high correction effectiveness while maintaining manageable system complexity through modular processing.
Solution Approach 2:
The patent changes processing parameters dynamically based on the detected noise type and severity. The system adjusts key parameters such as denoising strength, sharpness enhancement, and contrast adjustment according to the specific contamination characteristics in each region. This parameter adaptation allows effective differentiation between contamination types without requiring completely separate processing pipelines for each scenario.
3Reliability
If deep learning networks are used for image reconstruction, then image quality is improved, but processing time increases and device complexity increases
Solution Approach 1:
The patent applies preliminary action by performing noise classification and region segmentation before invoking deep learning-based image reconstruction. The system first identifies contaminated regions and characterizes the noise type using computationally efficient algorithms, then prepares appropriate processing parameters and masks. This preliminary preparation allows the subsequent deep learning reconstruction to focus only on necessary regions with optimized parameters, significantly reducing processing time compared to applying full-image deep learning reconstruction without prior preparation.
Data Source
AI summary
A method for image capture using artificial intelligence (AI) techniques to generate a user-personalized and noise-corrected final image from a captured image, that includes classifying a noise associated with a lens of an imaging device, preprocessing the captured image based on the classified noise to determine an initial region of interest (ROI), generating a first processed image by inputting the initial ROI and the captured image to a deep learning network, receiving a passive user input corresponding to a portion of a first preview of the first processed image, determining an additional ROI based on the passive user input and the classified noise, generating a second processed image by inputting the second ROI and the captured image to the deep learning network, and generating a user-personalization based noise-corrected final image based on the second processed image.