背景を削除 — 無料AIツール
AIで写真の背景を削除。100%ブラウザ内処理 — ファイルはアップロードされません。
Drop image to remove background
PNG, JPG, WebP, BMP • Max 25MB
How It Works
This tool uses a state-of-the-art AI segmentation model that runs directly in your browser using WebGPU acceleration. Unlike cloud-based services like remove.bg or Canva, the entire neural network is downloaded once and runs locally — your images are never uploaded to any server, and processing happens in 1-3 seconds on modern hardware.
The AI analyzes your image at multiple resolution scales simultaneously. At the coarsest level, it identifies the overall subject silhouette. At finer levels, it detects intricate details like individual hair strands, semi-transparent edges, and subtle color transitions between foreground and background. The result is a high-resolution alpha matte that produces clean, professional cutouts.
After the initial model download (~30MB), the AI engine is cached in your browser. Return visits process images instantly with no loading delay. The model handles portraits, product photos, animals, objects, logos, and complex multi-subject scenes — automatically detecting the primary foreground element.
The output is a transparent PNG at the original image resolution. You can download it directly or use it as a starting point for further editing in Photoshop, Figma, Canva, or any image editor that supports transparency.
Features
- ✓AI-powered edge detection for precise cutouts around hair, fur, and complex edges
- ✓WebGPU acceleration for near-instant processing on modern browsers
- ✓One-click download as transparent PNG at full resolution
- ✓Handles portraits, product photos, logos, animals, and complex scenes
- ✓Model caches locally — loads once (~30MB), processes instantly after
- ✓No file size limits for most images — recommended up to 25MP
- ✓Works offline after initial model download
- ✓Zero watermarks, zero signup, unlimited free uses
How to Use This Tool
- Open the Background Remover and drag your image onto the upload area, or click to browse your files. Supported formats include JPG, PNG, WebP, and BMP.
- Wait 2-3 seconds while the AI model processes your image entirely within your browser — no upload occurs. The first use requires a one-time model download (~30MB) which is cached for future visits.
- Preview the result with the transparent background. The checkerboard pattern indicates transparent areas where the background has been removed.
- Download the cutout as a high-resolution transparent PNG — ready for e-commerce listings, social media posts, presentations, or further editing in your preferred design tool.
Perfect For
- →E-commerce sellers creating clean product photos on white backgrounds for Amazon, Etsy, and Shopify listings
- →Social media managers removing backgrounds for Instagram stories, TikTok thumbnails, and Facebook ad creatives
- →Real estate agents and property managers preparing listing photos with clean, professional backgrounds
- →Graphic designers creating composite images, collages, and layered designs in Photoshop or Figma
- →Job seekers creating professional headshot photos with solid backgrounds for LinkedIn profiles and resumes
- →YouTubers and streamers creating transparent overlays, custom thumbnails, and channel art
- →Marketing teams preparing product images for email newsletters, landing pages, and digital advertising campaigns
- →Teachers and students creating clean presentation materials and educational visual assets
Under the Hood
The background removal uses a U²-Net (U-squared Net) deep learning model, a nested U-structure architecture specifically designed for salient object detection. Unlike general-purpose segmentation models, U²-Net captures fine-grained details at multiple scales through its two-level nested U-structure, making it exceptionally accurate on complex edges like hair, fur, and semi-transparent materials.
The model was trained on the DUTS-TR dataset containing over 10,000 human-annotated images, plus additional training data covering portraits, products, animals, and complex scenes. During inference, the network generates a saliency probability map where each pixel receives a score between 0.0 (background) and 1.0 (foreground). This probability map becomes the alpha channel in the output PNG.
For browser-side inference, the model is compiled to ONNX format and executed via WebGPU — the next-generation graphics API that provides direct GPU compute access from the browser. WebGPU replaces the older WebGL-based inference path, delivering 3-5x faster processing on supported hardware. On devices without WebGPU support, the tool falls back to CPU-based inference via WebAssembly.
Privacy is architectural, not policy-based. The entire inference pipeline — model weights, image preprocessing, neural network execution, and alpha matte postprocessing — runs inside the browser sandbox. No network requests are made after the initial model download. This makes the tool GDPR, CCPA, and HIPAA compliant by design — there is no user data to protect because no user data is ever collected or transmitted.