AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
TL;DR
This work proposes albedo and shading gradient descriptors which are derived from physics-based models and is the first to directly address the texture and intensity ambiguity problems of the shading estimations.
Image denoising aims to restore clean, high-fidelity visual structures from noisy inputs. Although deep learning (DL) approaches have dominated state-of-the-art benchmarks, they often incur heavy computational overhead, massive parameter counts, and a lack of mathematical transparency. To tackle these limitations, we p...
Traditional image registration methods rely on handcrafted feature extraction and matching strategies, which are inherently subjective and fail to fully exploit data-driven mechanisms. To address these limitations, we propose a novel dual-channel network for optical and synthetic aperture radar image registration. Our...
Visual perception in dynamic scenes is a crucial task in the fields of computer vision and autonomous driving. In this paper, we focus on monocular optical flow and expansion estimation. Optical flow describes the positional changes of pixels between two frames, while optical expansion describes local image-scale chang...
Low-light image enhancement aims to improve image quality under insufficient illumination while recovering underlying structural and texture information. Existing approaches, ranging from conventional image processing techniques to deep generative models, have shown promising performance in brightness enhancement and d...
Feature extraction is a common point at which current image fusion approaches fall short in establishing relationships between local and global information. The result is merged photos of low quality. This research presents an interactive transformer-based infrared and visible image fusion network to solve this problem...
It is shown that domain-shift immunity is an inherent, largely architecture-agnostic property of deep de-formable registration when trained with a robust pipeline and offered a principled explanation for the cross-domain generalizability of deep registration networks.
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