CNN Image Segmentation for Region-Specific Noise Reduction
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Solution Overview
Problem
Conventional image processing technologies using convolutional neural networks (CNNs) struggle to accurately reduce noise in images, particularly when different types of noise occur in different regions, leading to insufficient noise reduction in less noisy areas and blurring of boundaries between noisy and less noisy regions.
Innovation Solution
An image processing device that segments input images into regions using a first CNN, generates a first image by converting pixel values into a specific value for each region, and performs noise reduction using a second CNN, allowing for more accurate processing by embedding region information and enabling branch processing that typical CNNs cannot perform.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional CNNs are used for noise reduction, then processing is simple and fast, but noise reduction accuracy is insufficient and boundaries blur
Solution Approach 1:
The input image is segmented into multiple regions based on noise characteristics before processing. Each region is identified and classified according to its noise level, allowing subsequent targeted noise reduction processing to be applied to each segment independently, thereby improving overall noise reduction accuracy without excessive complexity
Solution Approach 2:
Different noise reduction processing parameters and intensities are applied to different regions of the image based on their specific noise characteristics. This local quality approach ensures that each region receives appropriate processing tailored to its noise level, preventing boundary blurring while maintaining high accuracy in noisy areas
2Reliability
If uniform noise reduction is applied to the entire image, then processing is simple, but noisy regions are not adequately reduced and less noisy regions become over-processed
Solution Approach 1:
The image is divided into multiple regions with different noise characteristics through segmentation. This allows the system to identify which regions require noise reduction and which do not, enabling reliable and effective noise reduction without uniformly processing the entire image
Solution Approach 2:
Noise reduction processing is applied selectively to specific regions based on their noise characteristics. Regions with high noise levels receive appropriate reduction processing, while regions with low noise levels are either excluded from processing or receive minimal processing, ensuring reliability without over-processing
3Measurement precision
If region-based processing is implemented, then noise reduction accuracy improves, but processing time and computational load increase
Solution Approach 1:
The image is segmented into regions based on noise characteristics, allowing parallel processing of multiple regions. This segmentation approach improves noise reduction accuracy while the parallel processing capability helps mitigate the increase in processing time by distributing computational load across different regions simultaneously
Data Source
AI summary
According to one embodiment, an image processing device includes a storage and an image processor. The storage stores therein an input image. The image processor segments the input image into a plurality of regions by using a first convolutional neural network (CNN), generates a first image by converting pixel values of pixels in a first region included in the regions into a first value, and performs image processing on the first image by using a second CNN to generate a second image.