Joint unsupervised object segmentation and inpainting
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Solution Overview
Problem
Existing methods for unsupervised object segmentation and inpainting face challenges with unstable training processes, particularly on higher resolution images, and require significant human intervention for tasks like trimap generation and object-background separation.
Innovation Solution
A novel SEIGAN architecture that combines neural networks for segmentation, enhancement, and inpainting, using a swap network trained end-to-end with multiple loss functions to achieve stable and high-resolution object-background manipulation, including segmentation, enhancement, and inpainting.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If GAN-based techniques are used for unsupervised object segmentation, then segmentation accuracy is improved, but training stability deteriorates on higher resolution images
Solution Approach 1:
The model segments the image into object and background regions using a segmentation network that outputs segmentation masks. This segmentation step separates the complex task of object-background separation into manageable components, allowing each to be processed independently while maintaining overall stability even at high resolutions
Solution Approach 2:
The approach transitions from direct pixel-level manipulation to latent space operations by encoding images into latent representations. The swap network operates in this latent dimension, swapping object and background features before decoding, which stabilizes training by avoiding direct high-resolution pixel conflicts
2Ease of operation
If traditional trimap generation and object-background separation methods are used, then manual control is improved, but automation level deteriorates
Solution Approach 1:
The system performs automatic trimap generation and object-background separation without requiring manual intervention. The segmentation network automatically identifies object regions, and the swap network autonomously separates and recombines object and background components, eliminating the need for manual trimap creation while maintaining high-quality results
3Manufacturing precision
If swap network with multiple loss functions is used, then image transformation quality is improved, but computational complexity deteriorates
Solution Approach 1:
The model combines segmentation, enhancement, and inpainting networks into a unified swap network architecture. Multiple loss functions (adversarial loss, reconstruction loss, and segmentation loss) are combined into a single composite loss that guides the joint optimization of all network components, improving image transformation quality while managing computational complexity through integrated training
Data Source
AI summary
The invention relates to implementation of image processing functions associated with finding the boundaries of objects, removing objects from an image, inserting objects into an image, creating new images from a combination of existing images. Proposed is a method for automated image processing and a computing system for performing automated image processing, comprising: first neural network for forming a coarse image z by segmenting an object O from an original image x containing the object O and background Bx by a segmentation mask, and, using the mask, cutting off the segmented object O from the image x and pasting it onto an image y containing only background By, second neural network for constructing an enhanced version of an image ŷ with pasted segmented object O by enhancing coarse image z based on the original images {circumflex over (x)} and y and the mask m; third neural network, for restoring the background-only image {circumflex over (x)} without removed segmented object O by inpainting image obtained by zeroing out pixels of image x using the mask m; wherein the first, second and third neural networks are combined into common architecture of neural network for sequential performing segmentation, enhancing and inpainting and for simultaneously learning, wherein the common architecture of neural network accepts the images and outputs processed images of the same dimensions.