Latent Space Imaging

2024-07-09

arXiv.org

AI Summary

Traditional imaging relies on high-bandwidth pixel-grid sampling, whereas human vision achieves efficient information compression via perceptual coding. This paper proposes a latent-space-driven end-to-end imaging paradigm that jointly optimizes optical acquisition and generative models (e.g., StyleGAN) in their semantic latent space, enabling hardware-level encoding of low-dimensional, semantically rich representations. We introduce a semantic-guided single-pixel amplitude modulation scheme that breaks the constraints of regular pixel grids; leveraging linearly separable decision boundaries in pre-trained model latent spaces, we achieve task-adaptive reconstruction. Experiments demonstrate raw imaging compression ratios of 1:100–1:1000, and downstream task-specific compression ratios (e.g., classification, retrieval) up to 1:16,384. This significantly reduces bandwidth, storage, and hardware complexity, while enabling high-speed and task-customized imaging.

Technology Category

• Computer Vision: Multi-modal Vision
• Machine Learning: Deep Generative Models & Autoencoders
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

Digital imaging systems have traditionally relied on brute-force measurement and processing of pixels arranged on regular grids. In contrast, the human visual system performs significant data reduction from the large number of photoreceptors to the optic nerve, effectively encoding visual information into a low-bandwidth latent space representation optimized for brain processing. Inspired by this, we propose a similar approach to advance artificial vision systems. Latent Space Imaging introduces a new paradigm that combines optics and software to encode image information directly into the semantically rich latent space of a generative model. This approach substantially reduces bandwidth and memory demands during image capture and enables a range of downstream tasks focused on the latent space. We validate this principle through an initial hardware prototype based on a single-pixel camera. By implementing an amplitude modulation scheme that encodes into the generative model's latent space, we achieve compression ratios ranging from 1:100 to 1:1000 during imaging, and up to 1:16384 for downstream applications. This approach leverages the model's intrinsic linear boundaries, demonstrating the potential of latent space imaging for highly efficient imaging hardware, adaptable future applications in high-speed imaging, and task-specific cameras with significantly reduced hardware complexity.

Problem

• Reducing bandwidth and memory in digital imaging systems
• Encoding image data into generative model latent space
• Achieving high compression ratios for efficient imaging hardware

Innovation

• Encodes image data into generative model latent space
• Uses amplitude modulation for high compression ratios
• Leverages model's linear boundaries for efficient imaging