Image Sensing Device Interpolated Refined Convolution Layers
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Solution Overview
Problem
Current image sensing devices face challenges in generating high-quality output images from raw images due to limitations in interpolation and refinement processes, particularly in maintaining color filter pattern alignment and effectively learning weights for convolution layers.
Innovation Solution
An image sensing device comprising modules that generate interpolated and refined images using convolution layers, with each module learning weights to improve image clarity and maintain color filter pattern alignment, enabling end-to-end learning for enhanced output image quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional interpolation methods are used to generate images from raw sensor data, then processing speed is maintained, but image quality and clarity deteriorate
Solution Approach 1:
The image processing pipeline is segmented into multiple specialized modules: a first module for generating interpolated images using convolution layers, a second module for generating refined images, and a third module for generating final output images. Each module focuses on a specific aspect of image enhancement, allowing for optimized processing at each stage while maintaining overall image quality.
Solution Approach 2:
The first module performs preliminary interpolation to generate intermediate images from raw sensor data before further refinement. By pre-processing the raw data into structured interpolated images with proper color filter alignment, subsequent refinement stages can operate more efficiently on already-organized data, improving both quality and speed.
2Measurement precision
If multiple convolution layers are used to improve image refinement, then image clarity is improved, but device complexity increases
Solution Approach 1:
The complex convolutional processing is divided into three distinct modules, each with specific convolution layers dedicated to particular tasks. This segmentation allows each module to be optimized independently and makes the overall system more manageable and implementable despite the multiple layers involved.
Solution Approach 2:
The system employs dynamic weight learning in the second and third modules, where convolution layer weights are learned through training rather than being fixed. This allows the system to adapt to different input conditions and optimize image refinement for various scenarios, improving clarity while maintaining reasonable complexity through learned parameters.
3Measurement precision
If end-to-end learning is implemented across all modules, then overall image quality is enhanced, but training complexity and time increase
Solution Approach 1:
The end-to-end learning system is segmented into modules with different learning strategies. The first module uses fixed or partially fixed weights for interpolation, while the second and third modules use learned weights for refinement. This segmentation allows gradient flow through the entire system for end-to-end optimization while reducing training complexity in the interpolation stage.
Solution Approach 2:
The first module performs preliminary interpolation with fixed or semi-fixed weights to create structurally sound intermediate images. This preliminary action provides a solid foundation for the subsequent learned refinement stages, allowing the end-to-end learning to focus primarily on the refinement aspects rather than learning basic interpolation from scratch.
4Measurement precision
If color filter pattern alignment is maintained during interpolation, then color accuracy is improved, but processing complexity increases
Solution Approach 1:
The first module performs preliminary interpolation that specifically maintains color filter pattern alignment in the intermediate images. By establishing proper color alignment early in the processing pipeline, subsequent refinement modules can operate on already-aligned data without needing to re-establish color relationships, reducing their complexity while preserving color accuracy.
Data Source
AI summary
Disclosed is an image sensing device including a first module suitable for generating a plurality of interpolated images separated for each color channel, based on a raw image and a plurality of first convolution layers, a second module suitable for generating a plurality of refined images separated for each color channel, based on the plurality of interpolated images and a plurality of second convolution layers, and a third module suitable for generating at least one output image corresponding to the raw image, based on the plurality of refined images and a plurality of third convolution layers.