Unified Spatial Image Processing via Multi-Scale Decomposition
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Solution Overview
Problem
Current image-enhancement systems require sequential execution of discrete modules for various tasks, leading to computational inefficiency, latency, and complex parameter adjustments, limiting flexibility and effectiveness in achieving comprehensive image enhancement.
Innovation Solution
A unified approach that performs multiple image-enhancement tasks concurrently through multi-scale image decomposition, generating intermediate images like photographic masks and temporary images, which are then used with look-up tables to compute enhanced output images, reducing the need for sequential processing and simplifying parameter adjustments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If sequential execution of discrete image-enhancement modules is used, then each discrete task can be processed independently, but computational efficiency decreases and processing time increases
Solution Approach 1:
The patent combines multiple discrete image-enhancement modules (sharpening, contrast enhancement, denoising) into a single unified module that processes all tasks simultaneously through multi-scale decomposition, eliminating sequential execution overhead and improving computational efficiency
Solution Approach 2:
The unified image-enhancement module performs multiple functions (sharpening, contrast enhancement, denoising, lighting adjustment) within a single processing framework, allowing one module to replace many discrete modules while maintaining independent control over each enhancement task
2Adaptability or versatility
If global application of enhancement techniques is used, then processing is simpler, but flexibility and quality of local enhancement is limited
Solution Approach 1:
The patent applies different enhancement operations to different spatial regions and scales independently by computing intermediate images at multiple scales and selectively combining them, allowing local regions to receive customized enhancement while maintaining overall image coherence
Solution Approach 2:
The image is decomposed into multiple scales and frequency bands through multi-scale decomposition, enabling independent processing of different image regions and features, then reconstructed by selectively combining processed components at appropriate scales
3Reliability
If multiple discrete image-enhancement tasks are executed sequentially, then each task can be optimized independently, but total processing time and latency increase
Solution Approach 1:
The unified module performs multiple enhancement tasks in continuous parallel operations rather than sequential steps, with all processing operations occurring simultaneously within a single processing pass, eliminating idle time between tasks while maintaining full enhancement functionality
Data Source
AI summary
A method for enhancing an input image to produce an enhanced output image is provided. The method includes constructing a photographic-mask intermediate image without low-contrast details and a temporary-image intermediate image with enhanced mid-contrast details, retained high-contrast details, and reduced low-contrast details, and employing values for the photographic-mask intermediate image and temporary-image intermediate image to produce the enhanced output image that is globally and locally contrast-enhanced, sharpened, and denoised.