Accelerating block-matching and 3D filtering method for image denoising on GPUs

Abstract:

Denoising photographs and video recordings is an important task in the domain of image processing. In this paper, we focus on block-matching and 3D filtering (BM3D) algorithm, which uses self-similarity of image blocks to improve the noise-filtering process. Even though this method has achieved quite impressive results in the terms of denoising quality, it is not being widely used. One of the reasons is a fact that the method is extremely computationally demanding. In this paper, we present a CUDA-accelerated implementation which increased the image processing speed significantly and brings the BM3D method much closer to real applications. The GPU implementation of the BM3D algorithm is not as straightforward as the implementation of simpler image processing methods, and we believe that some parts (especially the block-matching) can be utilized separately or provide guidelines for similar algorithms.

BibTeX:

@article{honzatko_accelerating_2019,
    title = {{Accelerating block-matching and 3D filtering method for image denoising on GPUs}},
    author = {Honzátko, David and Kruliš, Martin},
    year = {2019},
    journal = {{Journal of Real-Time Image Processing}},
    number = {6},
    doi = {10.1007/s11554-017-0737-9},
    issn = {1861-8219},
    pages = {2273--2287},
    url = {https://doi.org/10.1007/s11554-017-0737-9},
    volume = {16},
}

Journal article