Neural Image Processing With Region-Specific Degradation Reduction
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Solution Overview
Problem
Existing deep neural network (DNN) methods are ineffective in reducing image degradation uniformly across local partial regions of an image, failing to account for varying noise levels and types.
Innovation Solution
An information processing apparatus that identifies partial regions of an image and adjusts image processing intensity using a neural network, allowing for localized degradation reduction by employing a cloud server for training and an edge device for inference, with specific region extraction and intensity adjustment units to enhance restoration precision.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If a single neural network performs image processing on the entire image, then the processing is simple and fast, but the degradation reduction is uniform and fails to address varying noise levels in different regions
Solution Approach 1:
The patent divides the input image into multiple partial regions using a region extraction unit, and processes each region separately through the neural network. This segmentation allows different regions with varying noise levels to receive appropriate processing intensity, improving degradation reduction precision without requiring complex adaptive processing for each region.
Solution Approach 2:
The patent applies local quality by processing different regions of the image with different processing intensities based on their specific degradation characteristics. The intensity adjustment unit modifies the processing intensity for each partial region according to its noise level, allowing high-noise regions to receive stronger processing while low-noise regions receive minimal processing, thus optimizing both precision and computational efficiency.
2Manufacturing precision
If the neural network processes the entire image uniformly, then the processing is straightforward, but regions with low noise are over-processed while regions with high noise are under-processed
Solution Approach 1:
The patent applies partial action by processing only the necessary portions of the image with appropriate intensity. The intensity adjustment unit reduces processing intensity for regions with low noise levels and increases it for regions with high noise levels, avoiding both over-processing of clean regions and under-processing of degraded regions, thus optimizing computational energy usage.
3Manufacturing precision
If the neural network configuration is changed to adapt to different regions, then the processing precision improves, but the model complexity and training requirements increase
Solution Approach 1:
The patent applies dynamics by making the processing intensity adjustable for different regions rather than changing the fundamental neural network configuration. The intensity adjustment unit dynamically modifies processing parameters based on region characteristics, allowing the same neural network to adapt to different regions through parameter adjustment rather than structural changes, thus maintaining model simplicity while achieving region-specific precision.
Data Source
AI summary
An information processing apparatus includes an identification unit configured to identify a partial region of an input image, and a processing unit configured to perform image processing for reducing degradation of the input image on the input image by inference using a neural network. The processing unit is configured to change the image processing between the partial region and another region.