Visual-Neural-Inspired Image Inpainting for Specific Objects-of-Interest Imaging

TL;DR

By significantly enhancing object consistency and semantic coherence across diverse inpainting models, this work not only advances efficient and target-aware image restoration but also fosters interdisciplinary convergence between brain-inspired computation and cutting-edge inpainting technologies.

Abstract

Conventional image inpainting techniques typically process entire images, which often leads to computational inefficiency and susceptibility to information redundancy, particularly in occluded or cluttered scenes. Inspired by cortical processing mechanisms, this study introduces a novel framework termed "Specific Object-of-Interest Imaging" (SIOI) to overcome these limitations. The proposed approach first extracts and encodes object-level representations from complex scenes, producing structural and semantic priors that can be seamlessly integrated into existing inpainting pipelines. Extensive evaluations on dedicated object datasets--Teapot, Elephant, Giraffe, and Zebra--demonstrate that models incorporating SIOI consistently outperform those without it across key metrics including SSIM, PSNR, MAE, and LPIPS. The framework also exhibits strong robustness under challenging conditions such as low illumination, high noise, multiple object occlusions, and motion blur. Furthermore, theoretical analysis grounded in cognitive neuroscience reveals meaningful connections between the "object-first" perceptual mechanism and dynamic feature modulation in visual cortical areas (V1-V4). By significantly enhancing object consistency and semantic coherence across diverse inpainting models, this work not only advances efficient and target-aware image restoration but also fosters interdisciplinary convergence between brain-inspired computation and cutting-edge inpainting technologies.