Spatially Variant Kernel Generation for Adaptive Image Processing
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Solution Overview
Problem
Existing image processing methods using deep neural networks face performance degradation when applying the same kernel to all pixels in an image, leading to increased computational requirements for adaptive image processing.
Innovation Solution
An image processing apparatus and method that utilize a convolutional neural network to obtain characteristic information of an image, divide it into groups, perform convolution operations, generate an attention map and spatially variant kernel based on output information, and apply these kernels to the image for adaptive processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the same kernel is applied to each pixel in an image using a deep neural network, then the image processing is simple and fast, but the image processing performance is degraded
Solution Approach 1:
The patent applies different kernels to different pixels or regions based on their local characteristics. Specifically, it divides the image into multiple regions and generates different kernels for each region according to local features, thereby improving image processing performance while maintaining reasonable complexity through localized adaptation rather than global uniformity.
Solution Approach 2:
The patent segments the image into multiple regions based on local characteristics, and processes each region with its own optimized kernel. This segmentation approach allows the system to achieve high processing performance by treating different regions differently, while the segmentation itself provides a structured way to manage the complexity of adaptive processing.
2Reliability
If adaptive image processing is performed by applying different kernels according to pixel features, then the image processing performance is improved, but the amount of computation increases
Solution Approach 1:
The patent implements partial adaptive processing by applying different kernels only to certain regions or pixels that benefit most from adaptation, rather than uniformly adapting all pixels. This partial action approach maintains improved processing performance for critical regions while reducing the overall computational burden compared to full adaptive processing.
Solution Approach 2:
The patent applies computational resources selectively to different regions based on their local characteristics and needs. By generating different kernels only for regions that require adaptive processing rather than uniformly across the entire image, the system achieves good processing performance while significantly reducing the total amount of computation required.
Data Source
AI summary
An image processing apparatus, including a processor configured to execute instructions stored in a memory to: obtain characteristic information of a first image, divide the characteristic information into a plurality of groups, input each group into a respective layer of a plurality of layers included in a convolutional neural network and perform a convolution operation using one or more kernels to obtain a plurality of pieces of output information, generate an attention map including weight information corresponding to each pixel included in the first image, based on the plurality of pieces of output information, generate a spatially variant kernel including a kernel corresponding to the each pixel, based on the attention map and a spatial kernel including weight information according to a position relationship between the each pixel and a neighboring pixel, and generate a second image by applying the spatially variant kernel to the first image.