Lens Flare Detection Circuit Using Raw Image Data
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Solution Overview
Problem
Existing image processing technologies face challenges in accurately detecting the locations of lens flares, such as the green ghost, in images without consuming significant CPU bandwidth and power, and often introduce artifacts or false positives.
Innovation Solution
A circuit for processing raw images includes a threshold circuit and a logic circuit. The threshold circuit compares pixel values with threshold values for each color channel, and the logic circuit generates bright light information indicating the likelihood of lens flares. This approach processes raw images directly, reducing the risk of introducing artifacts and minimizing resource consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If lens flare detection is performed using processed images (after correction/filtering), then detection accuracy may be improved, but CPU bandwidth and power consumption increase significantly
Solution Approach 1:
The patent applies preliminary action by performing lens flare detection on raw image data before any correction or filtering operations are applied. The detection circuit analyzes pixel values in the raw image format (Bayer pattern) to identify bright light spots that indicate lens flares, thereby avoiding the need to process already-corrected images which would consume additional CPU resources and power.
Solution Approach 2:
The patent extracts the lens flare detection function from the main image processing pipeline and implements it as a separate detection circuit that operates on raw image data. This extraction allows the detection to be performed independently of the correction and filtering stages, reducing the computational burden on the CPU and minimizing power consumption while maintaining detection accuracy.
2Use of energy by moving object
If lens flare detection is performed on raw images, then CPU bandwidth and power consumption are reduced, but detection accuracy may deteriorate due to lack of image processing
Solution Approach 1:
The patent applies local quality by focusing the detection algorithm on specific characteristics of raw image data - specifically analyzing pixel values in the Bayer pattern to identify bright light spots. The detection circuit examines local pixel regions and uses the inherent color information in the raw data (without requiring full color processing) to accurately identify lens flare locations, thereby maintaining detection accuracy while operating on raw images.
3Reliability
If image correction and filtering are applied before lens flare detection, then image quality is improved, but the risk of introducing artifacts and false positives increases
Solution Approach 1:
The patent performs lens flare detection as a preliminary action before image correction and filtering operations. By detecting lens flares in the raw image data before any processing is applied, the system can identify and handle flare locations without risking that subsequent correction operations will introduce artifacts or false positives. The detection circuit analyzes the raw Bayer pattern data to identify bright spots that would correspond to lens flares, ensuring accurate detection before any potentially harmful processing occurs.
Data Source
AI summary
Embodiments relate to identifying locations in an image where a green ghost is likely to occur by processing a raw version of the image. A block of pixels in the raw image is extracted and then each pixel value in the block is compared with a threshold value of a corresponding color channel. The compared results are then processed to generate bright light information for the block, indicating whether the green ghost is likely to be present at a location of the image diagonally opposed to the location of the block in the image relative to an optical center of an image sensor. By processing the raw image without subsequent processing prone to introducing artifacts and creating false positive, a likely location of the green orb or green ghost may be more accurately identified.