Hardware-friendly Model-based Filtering for Image Restoration
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Solution Overview
Problem
Existing image restoration techniques are time-consuming and computationally intensive, requiring individual tuning of image processing modules for each camera lens and color filter array (CFA) sensor combination, leading to sub-optimal performance and increased computational resources.
Innovation Solution
A hardware-friendly model-based non-linear filtering system using non-iterative maximum a posteriori (NMAP) universal demosaicking with offline filter parameter determination, which identifies convolutional operators based on camera lens and CFA sensor properties to perform multiple image processing tasks efficiently.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional iterative image restoration techniques are used, then image restoration accuracy can be improved, but computational complexity and processing time increase significantly
Solution Approach 1:
The patent pre-calculates and stores optimal filter parameters (convolutional kernels) for different camera lens and CFA sensor combinations offline. During actual image restoration, the system only needs to look up and apply the pre-determined filters, avoiding iterative computation while maintaining high accuracy. This transforms a computationally intensive online optimization problem into a simple parameter lookup and application process.
Solution Approach 2:
The patent transforms the image restoration problem from solving complex iterative equations to applying predefined convolutional filters with specific parameters. By changing the approach from iterative optimization to direct filter application with pre-determined parameters (kernels), the system achieves both speed and accuracy without computational complexity.
2Reliability
If individual tuning of image processing modules is performed for each camera lens and CFA sensor combination, then processing performance can be optimized, but tuning time and computational resources increase
Solution Approach 1:
The system performs filter parameter tuning offline in advance for different camera lens and CFA sensor combinations. The tuned parameters are stored in a database or lookup table. When processing images from specific camera setups, the system simply retrieves the pre-tuned parameters without performing time-consuming iterative optimization, thus achieving both high performance and fast processing.
Solution Approach 2:
The patent creates a universal filter parameter set that can be applied across different image restoration tasks (demosaicking, chroma denoising, deblurring) for a given camera lens and CFA sensor combination. This multi-functional parameter set eliminates the need for separate tuning of individual processing modules, reducing both tuning time and computational resources while maintaining optimized performance.
3Reliability
If multiple separate image processing modules are used for demosaicking, chroma denoising, and deblurring, then processing completeness can be ensured, but system complexity and computational cost increase
Solution Approach 1:
The patent combines multiple image processing functions (demosaicking, chroma denoising, and deblurring) into a single unified convolutional filtering operation. By merging these separate modules into one integrated process that applies pre-determined convolutional kernels, the system maintains complete processing functionality while significantly reducing system complexity and computational overhead compared to running multiple separate modules.
Data Source
AI summary
A device (e.g., an image sensor, camera, etc.) may identify a camera lens and color filter array (CFA) sensor used to capture an image, and may determine filter parameters (e.g., a convolutional operator) based on the identified camera lens and CFA sensor. For example, a set of kernels (e.g., including a set of horizontal filters and a set of vertical filters) may be determined based on properties of a given lens and/or q-channel CFA sensor. Each kernel or filter may correspond to a row of a convolutional operator (e.g., of a restoration bit matrix) used by an image signal processor (ISP) of the device for non-linear filtering of the captured image. The corresponding outputs from the horizontal and vertical filters (e.g., two outputs of the horizontal and vertical filters corresponding to an input channel associated with the CFA sensor) may then be combined using a non-linear classification operation.