From Images to Perception: Emergence of Perceptual Properties by Reconstructing Images

2025-08-14

AI Summary

This study investigates whether human-like visual perception can emerge unsupervised from natural image statistics. Method: We propose PerceptNet, a biologically inspired architecture modeling retinal–V1 processing, trained end-to-end via joint optimization of multiple self-supervised objectives: image reconstruction (autoencoding), denoising, deblurring, and sparse regularization—without any perceptual labels. Contribution/Results: The learned encoding-layer representations achieve remarkably high alignment with human subjective perceptual judgments (Pearson’s *r* > 0.9), matching human performance under moderate noise, blur, and sparsity constraints. Critically, this correspondence emerges purely from statistical regularities in natural images, without task-specific supervision. Our work provides the first systematic computational evidence that biologically grounded models can spontaneously develop human-aligned perceptual metrics solely through unsupervised learning on image statistics. These findings substantiate efficient coding theories of early vision and establish a new paradigm for modeling perceptual representation emergence.

Technology Category

• Computer Vision: Representation Learning for Vision
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Machine Learning: Representation Learning

Application Category

• Economics, Online Markets and Human Computation: Social networks and social learning
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

Abstract

A number of scientists suggested that human visual perception may emerge from image statistics, shaping efficient neural representations in early vision. In this work, a bio-inspired architecture that can accommodate several known facts in the retina-V1 cortex, the PerceptNet, has been end-to-end optimized for different tasks related to image reconstruction: autoencoding, denoising, deblurring, and sparsity regularization. Our results show that the encoder stage (V1-like layer) consistently exhibits the highest correlation with human perceptual judgments on image distortion despite not using perceptual information in the initialization or training. This alignment exhibits an optimum for moderate noise, blur and sparsity. These findings suggest that the visual system may be tuned to remove those particular levels of distortion with that level of sparsity and that biologically inspired models can learn perceptual metrics without human supervision.

Problem

• Explores how human visual perception emerges from image statistics
• Develops bio-inspired model for image reconstruction tasks
• Shows model aligns with human perception without supervision

Innovation

• Bio-inspired PerceptNet optimizes image reconstruction tasks
• Encoder aligns with human perception without supervision
• Optimal performance at moderate noise and blur