Image Signal Processor Multi-Scale Illuminance Map
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Solution Overview
Problem
Existing image signal processing technologies face challenges in efficiently enhancing image contrast in low-illuminance environments, often resulting in artifacts like halos and fading, and require significant hardware resources, making them unsuitable for mobile applications.
Innovation Solution
An image system and method that generates multi-scale images and performs fast global weighted least squares (FGWLS) operations iteratively on these images to extract a final illuminance map, preventing blocking artifacts and enhancing image quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If conventional FGWLS schemes are used to enhance image contrast in low-illuminance environments, then image quality is improved, but blocking artifacts occur and hardware resource consumption increases
Solution Approach 1:
The patent segments the image processing task by dividing the image into multiple scales (multi-scale decomposition). Instead of processing the entire image at full resolution, the algorithm processes images at different resolutions and combines the results. This segmentation approach prevents blocking artifacts by distributing the processing load across multiple scales rather than concentrating it at a single resolution level.
Solution Approach 2:
The patent introduces a new dimension to the processing by implementing an iterative refinement process that operates across multiple scales. The algorithm transitions from a single-scale approach to a multi-scale iterative approach, adding the dimension of scale iteration. This allows the system to progressively refine the illuminance map across different resolutions, eliminating blocking artifacts while maintaining image quality.
2Manufacturing precision
If conventional FGWLS schemes are used to enhance image contrast, then image quality is improved, but hardware resource usage increases making it unsuitable for mobile applications
Solution Approach 1:
The patent segments the computational workload by dividing it across multiple scales and iterations. Instead of performing a single computationally intensive FGWLS operation at full resolution, the algorithm performs simplified operations at multiple lower resolutions and combines them. This segmentation of computation significantly reduces hardware resource consumption while maintaining image quality, making the solution suitable for mobile applications with limited processing power and energy resources.
Solution Approach 2:
The patent applies partial action by performing FGWLS operations selectively at different scales rather than processing the entire image at full resolution. The iterative refinement process applies computation only where needed across different scales, reducing the total computational burden. This partial processing approach maintains essential image quality while dramatically reducing hardware resource usage for mobile deployment.
3Manufacturing precision
If multi-scale images are generated and iterative FGWLS operations are performed, then blocking artifacts are prevented and image quality is enhanced, but processing complexity increases
Solution Approach 1:
The patent segments the complex processing task into manageable components: multi-scale decomposition, iterative FGWLS operations at each scale, and result combination. By breaking down the overall processing into these segmented stages, the algorithm manages complexity systematically. Each stage performs a specific function with well-defined inputs and outputs, making the overall complex process more controllable and implementable despite the increased processing requirements.
Solution Approach 2:
The patent manages processing complexity by introducing the dimension of multi-scale iteration. Instead of attempting to solve the entire problem at a single high resolution, the algorithm distributes the computational complexity across multiple scales and iterations. This dimensional approach transforms a single complex operation into multiple simpler operations that are easier to implement and manage, even though the total processing steps increase.
Data Source
AI summary
Provided is an operation method of an image signal processor (ISP) configured to perform signal processing on a raw image received from an image device, the operation method including generating a plurality of multi-scale images based on an input image, the plurality of multi-scale images having resolutions that are different from each other, iteratively performing a fast global weighted least squares (FGWLS) based operation on each of the plurality of multi-scale images to generate a final illuminance map, and outputting an enhanced image based on the final illuminance map and the input image.