Image Denoising via Segmented Neural Network Control
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Solution Overview
Problem
Existing methods for denoising media data, especially in low light scenarios, require repeated frame blending and are limited by fixed deep learning model parameters, making dynamic noise reduction based on captured conditions challenging.
Innovation Solution
A method that segregates images into regions based on noise reduction levels, using neural networks to vary control features and achieve requested noise reduction, with contextual information such as user inputs, face detection, and sensor metadata used to create alpha maps and control denoising strength.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Object-affected harmful factors
If multiple frames are blended to reduce noise in low light media data, then noise reduction is improved, but the processing time and complexity increase due to repeated blending operations
Solution Approach 1:
The system performs preliminary actions by capturing multiple frames in advance and preparing them for blending. The denoising operation is applied selectively based on predicted motion and regional analysis, rather than processing all frames equally, thus reducing unnecessary processing time while maintaining noise reduction effectiveness.
Solution Approach 2:
The image is divided into multiple regions with different motion characteristics. Different denoising operations are applied to different regions based on their motion levels. High-motion regions receive less aggressive denoising while low-motion regions receive stronger denoising, optimizing the balance between noise reduction and processing efficiency.
2Object-affected harmful factors
If deep learning models are used to reduce noise, then noise reduction effectiveness is improved, but the models cannot dynamically adapt to varying capture conditions due to fixed training parameters
Solution Approach 1:
The system introduces dynamic control features that allow the deep learning model to adapt to varying capture conditions in real-time. Control features such as exposure time, ISO sensitivity, and scene type are extracted and fed into the model, enabling it to adjust its denoising behavior dynamically without requiring retraining for each condition.
Solution Approach 2:
The system changes key parameters of the deep learning model during inference based on captured conditions. By modifying control features such as noise level estimates, motion blur parameters, and lighting conditions, the model can adapt its behavior to match the specific capture scenario, achieving versatility without retraining.
3Object-affected harmful factors
If uniform denoising is applied across the entire image, then noise reduction is simplified, but important details and edges may be lost
Solution Approach 1:
The system applies different denoising strengths to different regions of the image based on local characteristics. Regions with edges, textures, or high-frequency content receive lighter denoising to preserve details, while smooth regions with dominant noise receive stronger denoising. This local quality approach maintains image fidelity while effectively reducing noise.
Solution Approach 2:
The image is segmented into multiple regions based on motion analysis, edge detection, and texture characteristics. Each region is assigned a different denoising parameter set, allowing the system to preserve important features in critical regions while aggressively denoising in less important areas.
Data Source
AI summary
A method for processing image data, may include: receiving at least one image; segregating the at least one image into at least one region, based on a requested noise reduction level; and denoising the at least one image by varying at least one control feature of the segregated at least one region by a neural network to achieve the requested noise reduction level.