Dynamic Block Division for Accurate Distance Detection in Imaging Devices
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Solution Overview
Problem
Compact digital cameras with small image sensors struggle to achieve an effective bokeh effect due to the deepening depth of field at shorter focal lengths, as existing methods rely on spatial frequencies which can be misleading and fail to accurately distinguish in-focus areas from out-of-focus areas, especially with textured subjects.
Innovation Solution
An imaging device and method that includes an image blur evaluator, an imaging processor, an edge contour extraction processor, and a distance information detector, which captures multiple images at different lens positions, extracts edge contours, divides images into blocks based on blurring intensity, and detects distance information using contrast within these blocks to accurately apply the bokeh effect.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If spatial frequency evaluation is used to determine in-focus and out-of-focus areas, then the processing is simple and fast, but the accuracy of distance detection deteriorates due to misleading high frequencies from textured subjects
Solution Approach 1:
The image is divided into multiple blocks, and each block is further divided into sub-blocks for contrast evaluation. This segmentation allows the system to process local regions independently, maintaining processing efficiency while improving distance detection accuracy by evaluating contrast at multiple hierarchical levels rather than relying on misleading global spatial frequencies.
Solution Approach 2:
Edge contour components are extracted as an intermediary representation between the original image and distance information. By detecting edges first and then evaluating contrast in edge-containing blocks, the system creates a reliable intermediate step that eliminates the misleading effect of textures while preserving true focus information.
2Measurement precision
If the image is divided into small blocks, then the distance detection resolution is improved, but the processing time and computational load increase
Solution Approach 1:
The image is divided into blocks that are then subdivided into smaller sub-blocks (e.g., 2x2 or 4x4 subdivisions). This hierarchical segmentation enables the system to achieve high detection resolution by evaluating contrast at the sub-block level while maintaining processing efficiency through the structured, multi-level approach rather than processing every pixel individually.
Solution Approach 2:
The system performs contrast evaluation on selected sub-blocks rather than all possible block configurations. By strategically choosing which sub-blocks to evaluate based on edge contour presence and local contrast characteristics, the system achieves sufficient detection resolution without the computational burden of exhaustive analysis.
3Device complexity
If block size is fixed, then the processing is straightforward, but the accuracy deteriorates when camera shake causes blurring at different intensities across the image
Solution Approach 1:
The block division strategy is made dynamic by adjusting block sizes based on local image characteristics such as edge contour presence and blur intensity. Regions with strong edges and minimal blur use smaller blocks for high-resolution detection, while regions with camera shake-induced blurring use larger blocks to maintain accuracy. This dynamic adaptation resolves the contradiction between processing simplicity and detection accuracy.
Solution Approach 2:
Different block sizes are applied to different regions of the image based on local characteristics. Areas with high-frequency edge contours and low blur receive finer block divisions, while areas affected by camera shake receive coarser divisions. This local quality approach ensures optimal detection accuracy in each region without uniformly increasing processing complexity across the entire image.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach allows for accurate distance detection and application of the bokeh effect, improving the bokeh control process by reducing false contours and enhancing image processing speed, even in situations where the depth of field is ineffective.
Implementation Method 1
capturing a plurality of secondary images of the same object at different lens positions by driving a photographing lens
Implementation Method 2
detecting distance information of the objects captured in each block based on the contrast in each of the blocks
Data Source
AI summary
An imaging device is provided that includes an image blur evaluator, an imaging processor, an edge contour extraction processor, an image divider, and a distance information detector. The image blur evaluator evaluates the amount of blurring in an image. The imaging processor captures secondary images of the same object at different lens positions. The edge contour extraction processor extracts edge contour components of the secondary images and creates an edge-contour extracted image for each of the secondary images. The image divider divides each edge-contour extracted image into a plurality of relatively large blocks when blurring is evaluated to be relatively substantial, and into relatively small blocks when the blurring is evaluated to be moderate. The distance information detector detects distance information of the objects captured in each block based on the contrast.