Sub-pixel Depth Estimation via Neural Networks
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Solution Overview
Problem
Portable cameras face challenges in achieving depth of field effects due to hardware limitations, such as aperture and focal length constraints, making it difficult to obtain professional-quality depth of field in images.
Innovation Solution
A depth estimation method using a neural network-based approach that processes images from an image sensor with sub-pixels, obtaining viewpoint differences between paired sub-images and generating depth maps, which allows for edge detection and image segmentation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a portable camera uses standard image sensors without sub-pixel segmentation, then the device complexity remains low, but the depth estimation precision and depth of field effect quality deteriorate
Solution Approach 1:
Each pixel in the image sensor is divided into N sub-pixels (e.g., 4 sub-pixels per pixel), where each sub-pixel corresponds to a different viewpoint. This segmentation enables the capture of multiple sub-images from different perspectives simultaneously, providing the necessary data for accurate depth estimation without requiring multiple separate cameras or complex mechanical structures.
Solution Approach 2:
The patent introduces a new dimension by utilizing the spatial arrangement of sub-pixels within each pixel to encode depth information. Instead of relying solely on traditional aperture and focal length parameters, the system exploits the additional spatial dimension created by sub-pixel positioning to extract viewpoint differences and calculate depth maps.
2Measurement precision
If a portable camera uses multiple separate cameras to achieve depth estimation, then the depth estimation precision improves, but the device complexity and size increase
Solution Approach 1:
The patent merges multiple camera functions into a single image sensor by integrating N sub-pixels with different viewpoint offsets within each pixel. This consolidation achieves the depth estimation capability of a multi-camera system while maintaining a compact single-sensor architecture, reducing device complexity and size.
Solution Approach 2:
The image sensor performs multiple functions simultaneously: it captures color information, depth information, and viewpoint difference data all through a single sensor array. The sub-pixel structure enables the sensor to function as both a standard imaging sensor and a depth-sensing device, eliminating the need for separate depth cameras.
3Manufacturing precision
If professional cameras use large apertures and focal lengths to achieve depth of field effects, then the depth of field effect quality improves, but the device complexity and size increase
Solution Approach 1:
The patent replaces the mechanical optical approach (using large apertures and specific focal lengths to control depth of field) with a computational approach. By capturing viewpoint differences through sub-pixel segmentation and using neural networks to process this data, the system achieves depth of field effects through algorithmic processing rather than relying on complex optical mechanics.
Solution Approach 2:
The patent changes the fundamental parameters used to achieve depth of field effects. Instead of manipulating aperture size and focal length, the system utilizes viewpoint difference parameters extracted from sub-pixel data. This parameter transformation enables portable devices to achieve professional-quality depth of field effects without the hardware constraints of traditional optical systems.
Data Source
AI summary
A depth estimation method and apparatus are provided. The depth estimation method includes obtaining an image from an image sensor comprising upper pixels, each comprising N sub-pixels, obtaining N sub-images respectively corresponding to the N sub-pixels from the image, obtaining a viewpoint difference between the N sub-images using a first neural network, and obtaining a depth map of the image based on the viewpoint difference using a second neural network.