Pixel Processing Module Consolidating Edge Detection and Image Functions
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Solution Overview
Problem
Existing image processing systems face challenges with inconsistent edge detection across processing modules, difficulty in handling multiple edges within a kernel, and balancing contradictory processing functions like denoising and sharpening, which leads to artefacts and tuning issues.
Innovation Solution
Implementing a method that classifies pixel values into multiple groups within a kernel, allowing for consolidated processing of functions such as defective pixel detection, correction, denoising, sharpening, and demosaicing, using group indication data to simplify clustering and reduce latency and power consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple processing modules operate independently on pixel values, then each module can perform its specific function (denoising, sharpening, etc.), but inconsistent edge detection occurs across modules leading to artefacts
Solution Approach 1:
The patent combines multiple independent processing modules (defective pixel detection, denoising, sharpening, demosaicing) into a single integrated processing module that performs all functions. This unification ensures consistent edge detection across all processing operations by using a shared edge map, thereby eliminating artefacts caused by inconsistent edge handling between separate modules while maintaining the versatility of multiple processing functions.
2Productivity
If standard edge detection algorithms are used within a kernel, then processing can be performed, but only a single edge can be detected when multiple edges are present
Solution Approach 1:
The patent segments the kernel into multiple regions based on detected edges, allowing different processing operations to be applied to different regions. By identifying multiple edges within the kernel and creating region-based segmentation, the system can accurately handle multiple edges simultaneously while maintaining processing efficiency through the unified module architecture.
3Measurement precision
If clustering algorithms are used to group pixel values for processing, then multiple edges can be detected, but computational complexity and processing time increase significantly
Solution Approach 1:
The patent performs preliminary edge detection and kernel segmentation before applying processing functions. By pre-identifying edges and creating region segments in advance, the system avoids the need for complex real-time clustering during processing, thereby reducing computational complexity and processing latency while maintaining the capability to detect multiple edges.
4Adaptability or versatility
If separate processing modules are used for defective pixel detection, denoising, and sharpening, then each function can be optimized, but tuning becomes difficult due to contradictory aims
Solution Approach 1:
The patent merges multiple processing functions into a single integrated module with unified control. This allows centralized tuning of parameters that affect multiple functions simultaneously, resolving the contradictory aims between denoising (which smooths) and sharpening (which enhances edges). The unified module enables coordinated optimization where a single set of tuning parameters controls the interaction between conflicting processing functions.
Data Source
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AI summary
Data processing systems (e.g. image processing systems) and methods are provided for processing a stream of data values (e.g. pixel values). A processing module implements processing of a particular pixel value of the stream by operating on a particular subset of pixel values of the stream, by: classifying each of the pixel values within the particular subset into a group of a set of groups; processing the particular pixel value using one or more of the pixel values of the particular subset in dependence on the classification of the pixel values of the particular subset into the groups, wherein said processing the particular pixel value comprises performing, in a consolidated operation, multiple processing functions which depend upon the classification of pixel values of the particular subset into the groups; and outputting the processed particular pixel value.