Spatially Adaptive Image Filtering With Region-Based Kernel Selection
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Solution Overview
Problem
Convolutional neural networks require more training data and learnable parameters due to the content-agnostic nature of convolutional layers, leading to increased memory and runtime, and existing methods for content-adaptive filtering are inefficient or computationally expensive.
Innovation Solution
A spatially varying convolution method that selectively applies discrete and decorrelated filters to image subregions based on content, using a compact CNN to predict filter selection and enforce variability through regularization, allowing for content-based image enhancement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the same kernels are applied on all positions of any image irrespective of the content (spatial equivariance), then the convolutional operation can be implemented in an optimized and parallel way for fast processing, but more training data and learnable parameters are required to cope with content variability
Solution Approach 1:
The patent applies different convolutional kernels to different spatial regions of the image based on local content characteristics. Instead of using the same kernel globally, the system adapts the kernel selection to match the specific content in each region, thereby reducing the need for excessive training data while maintaining processing efficiency through localized adaptation rather than global complexity
Solution Approach 2:
The patent introduces dynamic kernel selection where the convolutional kernels are not fixed but are adaptively chosen based on the input image content. This dynamic adaptation allows the system to handle content variability without requiring a large number of static learnable parameters, as the same set of kernels can be dynamically selected to match different content types
2Adaptability or versatility
If more training data and learnable parameters are used to cope with content variability, then content-adaptive filtering can be achieved, but memory and runtime increase
Solution Approach 1:
By applying different kernels to different spatial regions based on local content, the system achieves content adaptivity without needing to learn separate parameters for every possible content type. This localized approach reduces the overall number of parameters that need to be trained and stored, thereby reducing memory requirements and training time while maintaining adaptability to various content types
Solution Approach 2:
The patent segments the image into different regions and applies appropriate kernel selection strategies to each segment. This segmentation allows the system to handle content variability in a modular way, processing each region with optimized parameters rather than using a large set of parameters for the entire image, thus reducing runtime and memory usage
3Adaptability or versatility
If a deep neural network with millions of parameters is used to predict convolutional kernels per pixel, then content-adaptive filtering is achieved, but the number of parameters to be learned becomes prohibitively large
Solution Approach 1:
Instead of predicting kernels for each individual pixel which would require millions of parameters, the patent applies different kernels to spatial regions or blocks of pixels based on their content characteristics. This regional approach maintains content adaptivity while dramatically reducing the number of parameters that need to be learned and stored
Solution Approach 2:
The patent uses a shared set of convolutional kernels that can be applied to multiple different content types and regions. This universal kernel set is selectively applied based on content, allowing the same parameters to serve multiple functions across different image regions and content types, thereby reducing the total number of parameters needed compared to having dedicated parameters for each pixel or content type
Data Source
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AI summary
An image processor for transforming an input image, the image processor being configured to implement a trained artificial intelligence model, wherein the image processor is configured to: receive the input image; based on one or both of (i) the content of the input image and (ii) features extracted from the input image, process the image by the trained artificial intelligence model to: (i) determine a set of image filters; and (ii) for each of a plurality of subregions of the image, select an image filter from the set of image filters; and for each of the plurality of subregions of the image, apply the respective image filter to the subregion or to features extracted from that subregion. This may allow for differentiable selection of filters from a discrete learnable and decorrelated group of filters to allow for content based spatial adaptations.