Image Denoising Model Training for Non-I.I.D. Noise Removal
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing image denoising methods, particularly Noise-to-Noise approaches, struggle with reduced performance when training data is limited, and fail to effectively remove non-independent and identically distributed noise, while traditional methods are time-consuming and inefficient.
Innovation Solution
A system and method for training a machine-learning algorithm using consecutive images, such as time-lapse imaging, to create a trained model that adjusts weights based on input and target images, incorporating random shifts to account for non-i.i.d. noise, utilizing convolutional neural networks like RCAN and U-Net for efficient denoising and interpolation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional image denoising methods are used, then denoising can be achieved, but the process is time-consuming and inefficient
Solution Approach 1:
The patent replaces traditional mechanical/image-processing denoising methods with a machine learning-based approach. The neural network model learns denoising patterns from training data and automatically applies them to new images, substituting manual or algorithmic processing with intelligent automation that achieves both high efficiency and quality results.
2Reliability
If Noise-to-Noise approaches are used with limited training data, then training can proceed, but denoising performance is reduced
Solution Approach 1:
The patent applies preliminary data augmentation techniques during the training phase to expand the effective training dataset. By applying random shifts, rotations, and other transformations to the available training images, the system creates additional synthetic training samples, allowing the model to learn robust denoising patterns even when the original training data is limited.
3Reliability
If conventional training methods are used, then training can be completed, but non-i.i.d. noise cannot be effectively removed
Solution Approach 1:
The patent implements dynamic training strategies where the model adapts to different noise characteristics during training. The training process incorporates varied noise patterns and transformations, allowing the neural network to learn flexible denoising behaviors that can handle non-i.i.d. noise scenarios. The model dynamically adjusts its processing based on the specific noise patterns it encounters in both training and inference phases.
Data Source
AI summary
A system for training a machine-learning algorithm for denoising images is configured to receive training data. The training data includes multiple image sets obtained from one or more series of consecutive images. Each image set includes a plurality of images obtained from a same series of consecutive images. The plurality of images of each image set includes an initial image, a middle image and a last image. The system is further configured to adjust weights of the machine-learning algorithm to obtain a trained machine-learning model, based on an output image of the machine-learning algorithm and a target image. The output image is obtained by using the initial image and the last image as input images. The target image is obtained from the middle image by applying a random shift to the middle image. The system is further configured to provide the trained machine-learning model.