Quality improvement
An ML model that efficiently detects and removes compression artifacts, enhances image quality while preserving vital visual elements
Overview
The Artifact remover is specifically designed to detect and eliminate a wide array of artifacts, primarily caused by heavy lossy compression, ensuring a remarkable enhancement of image quality and the preservation of essential visual elements.
The model is based on deep learning techniques that analyze and learn from vast and diverse datasets of images featuring various compression artifacts. Through this extensive training, the model becomes proficient in recognizing specific patterns and distortions linked to aggressive compression methods.
Once it receives an image, the model efficiently identifies the presence of compression artifacts and applies sophisticated image restoration algorithms to remove or reduce them. The restoration process restores crucial details, textures, and sharpness, resulting in an image with heightened clarity and visual appeal.
Use cases
Automatic artifact removal and quality improvement find valuable applications in various domains:
E-commerce platforms - In the world of online retail, image quality plays a crucial role in customer engagement and purchasing decisions. The model ensures that product images are of top-notch quality by removing compression artifacts, thus improving the overall shopping experience by delivering visually appealing product showcases.
Digital advertising - High-quality visuals are essential for successful digital advertising campaigns. Captivating ad campaigns with artifact-free images can boost engagement and strengthen the brand message.
Archives and galleries - Historical archives and art galleries often house valuable images that may have undergone degradation due to outdated compression techniques. Restoring such images ensures the preservation of their visual authenticity.
Printing and publishing - In print media image quality is crucial. By employing the model to remove compression artifacts, publishers can achieve clear, vivid images that resonate with readers and convey their intended message effectively.
API endpoints
An up-to-date reference with all API endpoints is available here:
Example API responses
| Input image | Input image |
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| Output preview | Output preview |
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| API response | API response |
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