Blind Document Image Denoising for Unpaired OCR Cleanup
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Solution Overview
Problem
Existing image denoising methods require paired noisy/clean images, which are rarely available, leading to inefficiencies in removing noise from scanned documents and degrading the performance of optical character recognition (OCR) technologies.
Innovation Solution
An end-to-end unsupervised multi-document blind image denoising model using a deep Mixture of Experts (MoE) integrated with a cycle-consistent Generative Adversarial Network (CycleGAN) that removes various noise types without requiring noisy/clean pairs, utilizing a single unified model to denoise and noisely generate images based on noise types and structure types.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If supervised denoising techniques are used, then denoising performance can be improved, but the requirement for paired noisy/clean images increases complexity and reduces applicability
Solution Approach 1:
The model performs self-supervised learning by generating its own training signals. The denoising network generates clean images from noisy inputs, and these generated clean images are then used to train the noising network, which in turn generates new noisy images. This self-generated training data eliminates the need for manually paired noisy/clean image datasets.
Solution Approach 2:
The clean image generator acts as an intermediary between the noisy image input and the final denoising task. It creates intermediate clean images that serve as training targets for the noising network, enabling the system to learn without requiring external paired data.
2Reliability
If multiple noise types are handled separately, then each noise type can be processed with specialized methods, but the system complexity and processing time increase
Solution Approach 1:
The denoising network is designed as a universal model that can handle multiple noise types (salt & pepper, blur, fade, watermarks) simultaneously. The network architecture uses shared convolutional layers that learn to remove different noise types through a unified training process, eliminating the need for separate specialized networks for each noise type.
Solution Approach 2:
The patent combines multiple denoising functions into a single end-to-end model that processes all noise types through one unified pipeline. The denoising network and noising network are merged into a coupled system that learns jointly, improving processing efficiency while maintaining reliability across different noise types.
3Ease of operation
If unsupervised methods are used to avoid paired data requirements, then ease of operation improves, but measurement precision of denoising may deteriorate
Solution Approach 1:
The system implements a feedback loop where the denoising network's outputs are evaluated and used to train the noising network, which then generates new training examples for the denoising network. This iterative feedback process allows the unsupervised model to progressively improve its denoising accuracy without requiring paired training data.
Solution Approach 2:
The denoising network performs preliminary denoising to generate clean images before the noising network adds noise back. This preliminary action creates high-quality intermediate results that serve as the foundation for generating diverse training examples, ensuring that the unsupervised learning process starts from accurate representations.
Data Source
AI summary
Methods and apparatus for end-to-end unsupervised multi-document blind image denoising is presented. The multi-document blind image denoiser removes various noise types from noisy documents without paired target cleaned documents and preserves the contents for optical character recognition. The end-to-end unsupervised multi-document blind image denoiser integrates a Mixture of Experts with a cycle-consistent GAN as the base network that effectively removes multiple types of noise, including salt & pepper noise, blurred and/or faded text, as well as watermarks from documents at various levels of intensity.