A comparative study of classical and deep learning image inpainting techniques for real-time restoration.
Navier-Stokes and Telea Fast Marching inpainting via OpenCV are benchmarked against a PyTorch encoder-decoder pipeline supporting LaMa and DeepFillv2 checkpoints. Both families are compared on PSNR, SSIM, MSE, latency and memory usage, with multi-resolution processing and model quantization explored for efficiency.
Deep inpainting produces better results than classical methods but costs orders of magnitude more compute. Which one is the right choice depends on constraints most papers never measure.
Benchmark both families on the same images across quality *and* cost — latency and memory alongside PSNR and SSIM — then test whether quantization and multi-resolution processing close the efficiency gap.
Classical Inpainting
Navier-Stokes and Telea Fast Marching methods via OpenCV.
Deep Learning Inpainting
PyTorch encoder-decoder pipeline supporting LaMa/DeepFillv2 checkpoints.
Signal-Quality Benchmarking
Compared via PSNR, SSIM, MSE, latency, and memory usage.
Optimization Exploration
Multi-resolution processing and model quantization for efficiency.
Classical
Deep Learning
Evaluation
Mask preparation
Damaged regions are defined for each test image.
Classical inpainting
Navier-Stokes and Telea methods reconstruct the region.
Deep inpainting
The PyTorch pipeline runs the same task.
Benchmarking
Quality and cost metrics are compared side by side.
Optimisation
Quantization and multi-resolution processing are evaluated.
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