Capsule Endoscope Image Sharpness via Depth-Based Deconvolution
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Solution Overview
Problem
Capsule endoscopes face challenges in capturing sharp images of both close and far objects within the gastrointestinal tract due to limited camera depth of field, which is exacerbated by the fixed focus nature of current devices and constraints of size and power, making it difficult to implement variable focus systems effectively.
Innovation Solution
The method involves using structured-light images to derive depth maps, which are then used to determine de-blurring filters for improving image sharpness by applying deconvolution kernels based on the point spread function of the camera system, allowing for the optimization of image quality across varying object distances.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If a fixed focus camera is used in a capsule endoscope, then the device size and power consumption are kept small, but the depth of field is limited and cannot produce sharp images over a required range of object distances
Solution Approach 1:
The image is divided into multiple depth regions (near field, mid field, far field) with different focus characteristics. Each region is processed independently with region-specific deconvolution filters, allowing sharp images across the entire depth range without requiring a physically adjustable focus mechanism.
Solution Approach 2:
Depth information is pre-acquired using structured light projection before regular image capture. This preliminary depth map is then used to guide the deconvolution filtering process, enabling the system to pre-compensate for focus variations across different object distances.
2Manufacturing precision
If variable focus implementation is attempted in a capsule endoscope, then image quality for different distances may improve, but the device size and power consumption increase due to additional components
Solution Approach 1:
The mechanical variable focus system is replaced with a digital post-processing system. Instead of physically moving lens elements or adjusting focus mechanisms, the patent uses computational deconvolution filtering based on pre-acquired depth information to achieve focus adjustment, eliminating complex mechanical components.
Solution Approach 2:
A depth map derived from structured light images serves as an intermediary between the fixed focus camera and the final sharp image. This depth information mediates the filtering process, allowing the system to compensate for fixed focus limitations without adding mechanical focus adjustment components.
3Manufacturing precision
If deconvolution filtering is applied to improve image sharpness, then image quality for specific depth regions improves, but processing time and computational complexity increase
Solution Approach 1:
Instead of applying a single global filter to the entire image, the patent applies local deconvolution filters tailored to each depth region. This allows efficient processing by focusing computational resources on region-specific characteristics rather than uniformly processing the whole image with maximum complexity.
Solution Approach 2:
Depth information is pre-acquired using structured light projection before regular image capture. This preliminary depth map is then used to guide the deconvolution filtering process, enabling the system to pre-compensate for focus variations across different object distances.
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 enables the capture of high-quality images for both near and far objects in a single frame, enhancing the ability to visualize details in the GI tract without the need for active navigation or significant changes to the endoscope design, thereby improving diagnostic capabilities.
Implementation Method 1
a regular image is received, where the regular image is captured using an imaging apparatus by projecting non-structured light onto a body lumen
Implementation Method 2
A first processed target region is generated by applying the de-blurring filter to the target region to improve sharpness of the target region
Implementation Method 3
One or more filter parameters of a de-blurring filter are determined from stored test pictures or from stored filter parameters according to the target distance
Data Source
AI summary
A method and apparatus for sharpening gastrointestinal (GI) images are disclosed. A target distance between the target region and the imaging apparatus is determined for a target region in the regular image. One or more filter parameters of a de-blurring filter are selected from stored filter parameters according to the target distance. A processed target region is generated by applying the de-blurring filter to the target region to improve sharpness of the target region. A method for characterizing an imaging apparatus is also disclosed. The imaging apparatus is placed under a controlled environment. Test pictures for one or more test patterns are captured at multiple test distances in a range including a focus distance using the imaging apparatus. One or more parameters associated a target point spread function are determined from each test picture for characterizing image formation of the imaging apparatus at the selected distance.