Automatic Spot Healing in Images via Pixel Threshold Masking
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Solution Overview
Problem
Current image editing technologies face challenges in accurately identifying and healing spots on images, particularly on mobile devices where precise touch inputs are difficult, leading to complex user interfaces and inefficient spot healing processes.
Innovation Solution
A computing device method that automatically identifies spots in images by calculating pixel values, determining a threshold, creating a mask, and replacing spot pixels with non-spot pixels, adapting to the size and shape of the selected area, and expanding the mask as needed to encompass the entire spot, without requiring manual brush size adjustments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual brush selection is used for spot healing, then user control over healing area is improved, but ease of operation deteriorates due to difficulty of precise touch inputs on mobile devices
Solution Approach 1:
The system performs automatic spot detection and mask generation without requiring manual brush selection. The computing device analyzes the image, identifies spots using pixel value comparison against threshold values, and creates masks automatically, eliminating the need for users to manually select areas with imprecise touch inputs.
Solution Approach 2:
The patent replaces the mechanical interaction of manual brush selection and touch input with an automated image processing system. The system uses pixel value analysis, threshold determination, and automatic mask creation algorithms to substitute the manual mechanical process of brush selection.
2Adaptability or versatility
If multiple UI controls and brush size adjustments are provided, then device versatility is improved, but device complexity increases
Solution Approach 1:
The patent extracts and removes unnecessary UI controls from the spot healing interface. By implementing automatic spot detection and mask generation, the system eliminates the need for brush size sliders, selection tools, and other controls that contribute to interface complexity while maintaining healing effectiveness.
Solution Approach 2:
The simplified interface provides universal functionality through automatic operation that adapts to different spot sizes and image types without requiring separate controls. The single tap gesture universally triggers the complete spot healing workflow regardless of the specific image or spot characteristics.
3Ease of operation
If automatic spot detection is implemented, then ease of operation is improved, but manufacturing precision deteriorates due to potential inaccuracies in automated identification
Solution Approach 1:
The system uses feedback mechanisms to improve spot detection accuracy. It compares pixel values against dynamically determined threshold values, refines mask generation based on detected spot characteristics, and iteratively improves identification accuracy while maintaining automatic operation.
Data Source
AI summary
Systems and methods are provided for automatically identifying and healing spots in images. In an embodiment, a method receives, at a computing device, a selection of an area of an image, the area having a center. The method includes calculating, by the computing device, pixel values of pixels in the selected area and determining, based at least in part on the calculated pixel values, a threshold value. The method further includes detecting, based at least in part on comparing pixel values in the selected area to the threshold value, one or more spots in the selected area. The method also includes creating, by the computing device, a mask for the one or more spots. The method replaces pixels in the mask with non-spot pixels.