I’ve been building AIEraser, a browser-based tool for removing unwanted elements from photos. One challenge has been making reconstructed areas blend naturally with nearby textures, especially when removing larger objects from detailed backgrounds. Early versions often left blurred patches, so I focused on improving contextual reconstruction while preserving the image’s original dimensions and sharpness. I also learned that selection flexibility matters. Some users prefer brushing over irregular objects, while others find box or automatic selection faster, so I added all three approaches. I’d appreciate feedback from anyone who has worked on image inpainting or object-removal tools. What types of images or backgrounds usually expose the biggest weaknesses in these models? I’m particularly interested in difficult test cases and suggestions for evaluating output quality. Step-by-step: 1. I built AIEraser as a browser-based tool for removing unwanted elements from photos. 2. I tested object removal on detailed backgrounds and identified blurred patches as a weakness, especially when removing larger objects. 3. I focused on improving contextual reconstruction while preserving the original image dimensions and sharpness. 4. I added brushing, box selection, and automatic selection to support different user preferences and object shapes. 5. I’m seeking difficult image and background test cases, along with suggestions for evaluating the quality of the output.