Endoscopic Video Histogram Enhancement with Targeted Sub-Block Processing
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Solution Overview
Problem
Existing image processing systems for endoscopy face challenges in accurately visualizing target treatment sites due to highly iterative histogram enhancement processes that burden image processors and cause delays, limiting the effectiveness of video image enhancement.
Innovation Solution
A method involving sub-block generation and prediction logic, using look-up tables and histogram enhancement techniques to enhance video images by dividing frames into sub-blocks, associating curves, and identifying targets within these sub-blocks to generate enhanced images.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If highly iterative histogram enhancement processes are applied to improve image quality, then image quality is improved, but image processor burden increases and processing delays occur
Solution Approach 1:
The image is divided into multiple sub-blocks, and histogram enhancement is applied independently to each sub-block rather than the entire image. This segmentation reduces the computational complexity of each processing step while maintaining overall image quality enhancement.
Solution Approach 2:
The patent applies histogram enhancement to only those sub-blocks that contain targets of interest, rather than processing all sub-blocks uniformly. This preliminary identification of relevant regions prevents unnecessary processing of empty or non-critical areas, reducing overall processing burden.
2Manufacturing precision
If highly iterative histogram enhancement processes are applied to enhance video images, then image quality is improved, but processing delays are caused
Solution Approach 1:
By segmenting the video frame into sub-blocks and processing only relevant sub-blocks containing targets, the patent reduces the total processing time required for histogram enhancement while maintaining image quality improvements in critical regions.
Solution Approach 2:
The patent applies histogram enhancement selectively to sub-blocks containing targets rather than uniformly across the entire image. This partial action approach focuses computational resources on areas where enhancement is most needed, reducing overall processing delay.
3Productivity
If sub-block processing with target identification is implemented, then processing burden is reduced, but device complexity increases
Solution Approach 1:
The patent divides the image processing task into sub-block processing units, each handling a specific region. This segmentation enables parallel processing and reduces the computational burden on individual processing units, improving overall processing efficiency despite the added complexity of managing multiple sub-blocks.
Solution Approach 2:
Target identification is performed as a preliminary step to determine which sub-blocks require histogram enhancement. This preliminary action enables the system to skip processing of non-critical sub-blocks, reducing overall processing burden while the added complexity of target detection is offset by the elimination of unnecessary processing steps.
Data Source
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AI summary
A method is provided for enhancing video images in a medical device. The method includes receiving a first image frame and a second image frame from one or more image sensors. The first image sub-blocks are generated by dividing the first image frame. At least one curve to the first image sub-blocks are associated based on one or more look-up tables. A target in at least one of the first image sub-blocks is identified. Second image sub-blocks are generated by dividing the second image frame. At least one curve is associated to the second image sub-blocks based on the one or more look-up tables. The target is identified in at least one of the second image sub-blocks. Histogram enhanced images of the target in the first image sub-blocks and the second image sub-blocks are generated. A video image stream is generated based on the histogram enhanced images of the target.