libdebayer 0.1.2

debayer images with CUDA
Documentation

libdebayer

A CUDA accelerated debayering library with C/C++ and Rust support.

Dev Setup

nix develop

This enters a dev shell with all of the necessary build dependencies.

Library Breakdown

libdebayer

This is a simple C API that wraps the underlying CUDA kernels. The C API assumes the images are already in GPU memory. libdebayer implements 3 debayering algorithms:

  • Malvar 2004
  • Bilinear (same as builtin OpenCV)
  • Saronic custom algorithm (TODO: more details here or delete since Malvar 2004 has the best performance)

libdebayercpp

This provides a higher level C++ API that performs cudaMemcpy's from host to device. An example of how to use this library is in benchmark/cpp.

libdebayer-rs

This provides a higher level Rust API with TryFrom traits to go from an OpenCV Mat to a debayered Mat. An example of how to use this library is in rust/examples/test_benchmark.rs. This test benchmark is identical to the benchmark/cpp program.

Run Rust Example

nix develop
cd rust
KODAK_FOLDER_PATH="<path-to-benchmark-kodak-files>" cargo run --example test_benchmark

Benchmark

libdebayer is benchmarked against OpenCV and NPP (Nvidia Performance Primitives). To run the benchmark for the Malvar 2004 implementation run the following: nix run .#kodak_benchmark_cpp

Benchmark Results

  • OCV-EA gets Average PSNR: 28.616 dB
  • NPP gets Average PSNR: 28.8522 dB
  • libdebayer CUDA kernel gets Average PSNR: 33.4554 dB (+4.8394dB)
  • Greatly reduces color fringing

Image Results

Example output from OpenCV-EA:

OpenCV-EA

Example output from NPP:

NPP

Example output from our CUDA kernel:

malvar2004