Parallel Curvature Filter for High Performance Image Processing

Recently, curvature filter (CF) has been developed to implicitly minimize curvature for image processing problems such as smoothing and denoising. In this paper, we propose a parallel curvature filter (PCF) that performs on GPU which is much faster than the original CF on CPU. Inspired by Convolution Neural Networks processed by GPU, the convolution operations in curvature filter computation can be similarly paralleled by GPU so that the PCF on a single GPU can process 33.2 Giga pixels per second.

Problem setting

Recently, curvature filter (CF) has been developed to implicitly minimize curvature for image processing problems such as smoothing and denoising. In this paper, we propose a parallel curvature filter (PCF) that performs on GPU which is much faster than the original CF on CPU. Inspired by Convolution Neural Networks processed by GPU, the convolution operations in curvature filter computation can be similarly paralleled by GPU so that the PCF on a single GPU can process 33.2 Giga pixels per second.

In the broader publication record, this work appears in IWAIT 2019, vol. 11049 (SPIE), 282–287. The visual notes below pair the paper’s original figures with a concise reading of the method, experimental setup, and reported results.

Method and visual evidence

The method operates on discrete geometry and is designed to preserve meaningful shape structure while filtering noise, estimating features, or improving downstream geometric processing.

The extracted figures below show the geometry-processing pipeline, representative shapes, and visual or numerical comparisons.

Method overview. This image is extracted from an embedded PDF image object on page 3, then recomposed for web display.

Representation and setup. This image is extracted from an embedded PDF image object on page 4, then recomposed for web display.

Experimental evidence. This image is extracted from an embedded PDF image object on page 4, then recomposed for web display.

Result comparison. This image is extracted from an embedded PDF image object on page 5, then recomposed for web display.

Additional visual result. This image is extracted from an embedded PDF image object on page 5, then recomposed for web display.

Results and impact

The evaluation reported in IWAIT 2019, vol. 11049 (SPIE), 282–287 uses the extracted figures above to show the method’s measurement, reconstruction, segmentation, matching, or diagnostic behavior on representative experiments. These visuals are paired with the paper’s quantitative or qualitative analysis to make the workflow easier to inspect from the homepage.

Source handling

I extracted 14 candidate image objects from paper.pdf and generated the compressed WebP figures used on this page.