WireAIR Image Restoration
Supports perception reliability by improving degraded visual inputs with lightweight restoration design.
system visual
Protected or source media unavailable. Architecture preview used instead.
System Overview
Efficient image restoration research with wide receptive fields, WireA attention, and CLIP-based semantic enhancement.
Research project.
Implementation Signals
System flow
A compact view of how inputs move through processing, orchestration, validation, and output.
Degraded Image
Wide Receptive Field
WireA Attention
CLIP Enhancement
Restored Output
Engineering decisions
Decision Record
Keep restoration efficient
Problem: Perception systems often need improved inputs without heavy model cost.
Approach: Use a compact architecture with attention and semantic enhancement.
Tradeoff: Research system, not presented as deployed product.
Outcome: Public LinkedIn details report 364K parameters and 20.7G FLOPs.
Results / Learnings
Model
364K parameters, 20.7G FLOPs, LinkedIn-provided.
Metrics
Reported PSNR/SSIM across super-resolution, denoising, and turbulence removal.
Progressive depth
This page keeps the outcome and architecture visible first. Implementation stack, decisions, constraints, and media are available below so technical depth is opt-in rather than forced.
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Open