Automated Neural Network Training for Image Denoising
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Traditional methods for training neural networks for image denoising are inefficient, requiring long computational times and human intervention for visual evaluation, leading to resource wastage and time consumption.
Innovation Solution
An automated training mechanism for neural networks that receives a decoded noisy image, computes errors, and stops training when the difference between the denoised and ground truth errors is within a predetermined threshold, eliminating the need for prolonged training and human evaluation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional training methods are used with visual inspection and manual selection, then image denoising quality can be evaluated, but computational time and energy resources are excessively consumed
Solution Approach 1:
The patent replaces the mechanical visual inspection process with an automated error metric computation system. Instead of manually evaluating denoised images, the system automatically computes error metrics (MSE, PSNR, SSIM) between the denoised image and the noisy input image, substituting human visual evaluation with computational error analysis that is both precise and efficient.
Solution Approach 2:
The training process becomes self-service by automatically stopping when the error metric threshold is met. The system autonomously monitors the error between denoised and noisy images during training iterations and terminates training automatically when the error falls below a predetermined threshold, eliminating the need for continuous human intervention and visual evaluation.
2Reliability
If training is run for multiple iterations with weight adjusting techniques, then denoising performance improves, but energy resources are wasted
Solution Approach 1:
The system implements feedback by continuously computing error metrics during training iterations and using this error information to control the training process. The error metric serves as feedback that indicates when the denoising performance has reached an acceptable level, allowing the system to stop training appropriately without wasting additional energy on unnecessary iterations.
Solution Approach 2:
The patent changes the parameter that controls training termination from a fixed number of iterations to a dynamic error metric threshold. Instead of running for a predetermined number of iterations regardless of performance, the system adjusts training duration based on the computed error between denoised and noisy images, optimizing energy consumption while maintaining denoising quality.
3Measurement precision
If visual evaluation of predicted images is performed manually, then quality assessment is possible, but human effort and time consumption increase
Solution Approach 1:
The patent replaces manual visual evaluation with automated error metric computation. Instead of requiring human operators to visually inspect and assess denoised images, the system automatically computes quantitative error metrics (MSE, PSNR, SSIM) that provide precise quality assessment without requiring any human visual inspection effort.
Data Source
AI summary
A method, apparatus and computer program product provide an automated neural network training mechanism. The method, apparatus and computer program product receive a decoded noisy image and a set of input parameters for a neural network configured to optimize the decoded noisy image. A denoised image is generated based on the decoded noisy image and the set of input parameters. A denoised noisy error is computed representing an error between the denoised image and the decoded noisy image. The neural network is trained using the denoised noisy error and the set of input parameters and a ground truth noisy error value is received representing an error between the original image and the encoded image. The ground truth noisy error value is compared with the denoised noisy error to determine whether a difference between the ground truth noisy error value and the denoised noisy error is within a pre-determined threshold.