The Experts below are selected from a list of 162 Experts worldwide ranked by ideXlab platform

Taizo Suzuki - One of the best experts on this subject based on the ideXlab platform.

  • Variable Macropixel Spectral-Spatial Transforms With Intra- and Inter-Color Decorrelations for Arbitrary RGB CFA-Sampled Raw Images
    IEEE Signal Processing Letters, 2020
    Co-Authors: Taizo Suzuki, Seisuke Kyochi
    Abstract:

    A raw image captured by a color filter array (CFA), such as a Bayer pattern, is usually compressed after demosaicing with some processings (denoising, deblurring, tone-mapping, and so on). However, since photographers, designers, and high-end users prefer to work with the raw image sampled by CFA (referred to as “raw image”) directly, a raw image should be compressed before demosaicing. For effective raw image compression, this study introduces variable macropixel spectral-Spatial Transforms (VMSSTs), that can successfully decorrelate not only Bayer raw images but any other pure-color (RGB) ones. The proposed VMSSTs are designed by the following two steps: 1) intra-color decorrelation and 2) inter-color decorrelation. In lossless compression with JPEG 2000, compared with methods which do not use Transforms, the VMSSTs reduced the average bitrates of three types of CFAs: from approximately 0.09 to 0.12 bpp for the modified Bayer CFA, from 0.25 to 0.65 bpp for the diagonal stripe CFA, and from 0.33 to 0.70 bpp for the Fujifilm X-Trans CFA due to their high color decorrelation efficiency. In addition, in lossy compression with JPEG 2000, compared with a rearranged method, the VMSSTs improved the average bitrates of the Bjontegaard delta by around 3.97%, 14.95%, and 18.65% for each CFA model, respectively. Although a data-dependent adaptive transformation, the Karhunen-Loeve transform (KLT), showed the best performance in lossy compression, the introduced VMSSTs have shown performances comparable to those of the KLT in lossless compression, despite their simple structures.

  • wavelet based spectral Spatial Transforms for cfa sampled raw camera image compression
    IEEE Transactions on Image Processing, 2020
    Co-Authors: Taizo Suzuki
    Abstract:

    Spectral–Spatial Transforms (SSTs) change a raw camera image captured using a color filter array (CFA-sampled image) from an RGB color space composed of red, green, and blue components into a decorrelated color space, such as YDgCbCr or YDgCoCg color space composed of luma, difference green, and two chroma components. This paper describes three types of wavelet-based SST (WSST) obtained by reorganizing all of the existing SSTs covered in this paper. First, we introduce three types of macropixel SST (MSST) implemented within each $2 \times 2$ macropixel. Next, we focus on two-channel Haar wavelet Transforms, which are simple wavelet Transforms, and three-channel Haar-like wavelet Transforms in each MSST and replace the Haar and Haar-like wavelet Transforms with Cohen–Daubechies–Feauveau (CDF) 5/3 and 9/7 wavelet Transforms, which are customized on the basis of the original pixel positions in 2D space. Although the test data set is not big, in lossless CFA-sampled image compression based on JPEG 2000, the WSSTs improve the bitrates by about 1.67%–3.17% compared with not using a transform, and the WSSTs that use 5/3 wavelet Transforms improve the bitrates by about 0.31%–0.71% compared with the best existing SST. Moreover, in lossy CFA-sampled image compression based on JPEG 2000, the WSSTs show about 2.25–4.40 dB and 26.04%–49.35% in the Bjontegaard metrics (BD-PSNRs and BD-rates) compared with not using a transform, and the WSSTs that use 9/7 wavelet Transforms improve the metrics by about 0.13–0.40 dB and 2.27%–4.80% compared with the best existing SST.

  • Wavelet-Based Spectral–Spatial Transforms for CFA-Sampled Raw Camera Image Compression
    IEEE transactions on image processing : a publication of the IEEE Signal Processing Society, 2019
    Co-Authors: Taizo Suzuki
    Abstract:

    Spectral–Spatial Transforms (SSTs) change a raw camera image captured using a color filter array (CFA-sampled image) from an RGB color space composed of red, green, and blue components into a decorrelated color space, such as YDgCbCr or YDgCoCg color space composed of luma, difference green, and two chroma components. This paper describes three types of wavelet-based SST (WSST) obtained by reorganizing all of the existing SSTs covered in this paper. First, we introduce three types of macropixel SST (MSST) implemented within each $2 \times 2$ macropixel. Next, we focus on two-channel Haar wavelet Transforms, which are simple wavelet Transforms, and three-channel Haar-like wavelet Transforms in each MSST and replace the Haar and Haar-like wavelet Transforms with Cohen–Daubechies–Feauveau (CDF) 5/3 and 9/7 wavelet Transforms, which are customized on the basis of the original pixel positions in 2D space. Although the test data set is not big, in lossless CFA-sampled image compression based on JPEG 2000, the WSSTs improve the bitrates by about 1.67%–3.17% compared with not using a transform, and the WSSTs that use 5/3 wavelet Transforms improve the bitrates by about 0.31%–0.71% compared with the best existing SST. Moreover, in lossy CFA-sampled image compression based on JPEG 2000, the WSSTs show about 2.25–4.40 dB and 26.04%–49.35% in the Bjontegaard metrics (BD-PSNRs and BD-rates) compared with not using a transform, and the WSSTs that use 9/7 wavelet Transforms improve the metrics by about 0.13–0.40 dB and 2.27%–4.80% compared with the best existing SST.

Marc Niethammer - One of the best experts on this subject based on the ideXlab platform.

  • ISBI - Efficient registration of pathological images: A joint PCA/image-reconstruction approach
    Proceedings. IEEE International Symposium on Biomedical Imaging, 2017
    Co-Authors: Xu Han, Xiao Yang, Stephen R. Aylward, Roland Kwitt, Marc Niethammer
    Abstract:

    Registration involving one or more images containing pathologies is challenging, as standard image similarity measures and Spatial Transforms cannot account for common changes due to pathologies. Low-rank/Sparse (LRS) decomposition removes pathologies prior to registration; however, LRS is memory-demanding and slow, which limits its use on larger data sets. Additionally, LRS blurs normal tissue regions, which may degrade registration performance. This paper proposes an efficient alternative to LRS: (1) normal tissue appearance is captured by principal component analysis (PCA) and (2) blurring is avoided by an integrated model for pathology removal and image reconstruction. Results on synthetic and BRATS 2015 data demonstrate its utility.

  • Efficient Registration of Pathological Images: A Joint PCA/Image-Reconstruction Approach
    arXiv: Computer Vision and Pattern Recognition, 2017
    Co-Authors: Xu Han, Xiao Yang, Stephen R. Aylward, Roland Kwitt, Marc Niethammer
    Abstract:

    Registration involving one or more images containing pathologies is challenging, as standard image similarity measures and Spatial Transforms cannot account for common changes due to pathologies. Low-rank/Sparse (LRS) decomposition removes pathologies prior to registration; however, LRS is memory-demanding and slow, which limits its use on larger data sets. Additionally, LRS blurs normal tissue regions, which may degrade registration performance. This paper proposes an efficient alternative to LRS: (1) normal tissue appearance is captured by principal component analysis (PCA) and (2) blurring is avoided by an integrated model for pathology removal and image reconstruction. Results on synthetic and BRATS 2015 data demonstrate its utility.

Xu Han - One of the best experts on this subject based on the ideXlab platform.

  • ISBI - Efficient registration of pathological images: A joint PCA/image-reconstruction approach
    Proceedings. IEEE International Symposium on Biomedical Imaging, 2017
    Co-Authors: Xu Han, Xiao Yang, Stephen R. Aylward, Roland Kwitt, Marc Niethammer
    Abstract:

    Registration involving one or more images containing pathologies is challenging, as standard image similarity measures and Spatial Transforms cannot account for common changes due to pathologies. Low-rank/Sparse (LRS) decomposition removes pathologies prior to registration; however, LRS is memory-demanding and slow, which limits its use on larger data sets. Additionally, LRS blurs normal tissue regions, which may degrade registration performance. This paper proposes an efficient alternative to LRS: (1) normal tissue appearance is captured by principal component analysis (PCA) and (2) blurring is avoided by an integrated model for pathology removal and image reconstruction. Results on synthetic and BRATS 2015 data demonstrate its utility.

  • Efficient Registration of Pathological Images: A Joint PCA/Image-Reconstruction Approach
    arXiv: Computer Vision and Pattern Recognition, 2017
    Co-Authors: Xu Han, Xiao Yang, Stephen R. Aylward, Roland Kwitt, Marc Niethammer
    Abstract:

    Registration involving one or more images containing pathologies is challenging, as standard image similarity measures and Spatial Transforms cannot account for common changes due to pathologies. Low-rank/Sparse (LRS) decomposition removes pathologies prior to registration; however, LRS is memory-demanding and slow, which limits its use on larger data sets. Additionally, LRS blurs normal tissue regions, which may degrade registration performance. This paper proposes an efficient alternative to LRS: (1) normal tissue appearance is captured by principal component analysis (PCA) and (2) blurring is avoided by an integrated model for pathology removal and image reconstruction. Results on synthetic and BRATS 2015 data demonstrate its utility.

Xiao Yang - One of the best experts on this subject based on the ideXlab platform.

  • ISBI - Efficient registration of pathological images: A joint PCA/image-reconstruction approach
    Proceedings. IEEE International Symposium on Biomedical Imaging, 2017
    Co-Authors: Xu Han, Xiao Yang, Stephen R. Aylward, Roland Kwitt, Marc Niethammer
    Abstract:

    Registration involving one or more images containing pathologies is challenging, as standard image similarity measures and Spatial Transforms cannot account for common changes due to pathologies. Low-rank/Sparse (LRS) decomposition removes pathologies prior to registration; however, LRS is memory-demanding and slow, which limits its use on larger data sets. Additionally, LRS blurs normal tissue regions, which may degrade registration performance. This paper proposes an efficient alternative to LRS: (1) normal tissue appearance is captured by principal component analysis (PCA) and (2) blurring is avoided by an integrated model for pathology removal and image reconstruction. Results on synthetic and BRATS 2015 data demonstrate its utility.

  • Efficient Registration of Pathological Images: A Joint PCA/Image-Reconstruction Approach
    arXiv: Computer Vision and Pattern Recognition, 2017
    Co-Authors: Xu Han, Xiao Yang, Stephen R. Aylward, Roland Kwitt, Marc Niethammer
    Abstract:

    Registration involving one or more images containing pathologies is challenging, as standard image similarity measures and Spatial Transforms cannot account for common changes due to pathologies. Low-rank/Sparse (LRS) decomposition removes pathologies prior to registration; however, LRS is memory-demanding and slow, which limits its use on larger data sets. Additionally, LRS blurs normal tissue regions, which may degrade registration performance. This paper proposes an efficient alternative to LRS: (1) normal tissue appearance is captured by principal component analysis (PCA) and (2) blurring is avoided by an integrated model for pathology removal and image reconstruction. Results on synthetic and BRATS 2015 data demonstrate its utility.

Stephen R. Aylward - One of the best experts on this subject based on the ideXlab platform.

  • ISBI - Efficient registration of pathological images: A joint PCA/image-reconstruction approach
    Proceedings. IEEE International Symposium on Biomedical Imaging, 2017
    Co-Authors: Xu Han, Xiao Yang, Stephen R. Aylward, Roland Kwitt, Marc Niethammer
    Abstract:

    Registration involving one or more images containing pathologies is challenging, as standard image similarity measures and Spatial Transforms cannot account for common changes due to pathologies. Low-rank/Sparse (LRS) decomposition removes pathologies prior to registration; however, LRS is memory-demanding and slow, which limits its use on larger data sets. Additionally, LRS blurs normal tissue regions, which may degrade registration performance. This paper proposes an efficient alternative to LRS: (1) normal tissue appearance is captured by principal component analysis (PCA) and (2) blurring is avoided by an integrated model for pathology removal and image reconstruction. Results on synthetic and BRATS 2015 data demonstrate its utility.

  • Efficient Registration of Pathological Images: A Joint PCA/Image-Reconstruction Approach
    arXiv: Computer Vision and Pattern Recognition, 2017
    Co-Authors: Xu Han, Xiao Yang, Stephen R. Aylward, Roland Kwitt, Marc Niethammer
    Abstract:

    Registration involving one or more images containing pathologies is challenging, as standard image similarity measures and Spatial Transforms cannot account for common changes due to pathologies. Low-rank/Sparse (LRS) decomposition removes pathologies prior to registration; however, LRS is memory-demanding and slow, which limits its use on larger data sets. Additionally, LRS blurs normal tissue regions, which may degrade registration performance. This paper proposes an efficient alternative to LRS: (1) normal tissue appearance is captured by principal component analysis (PCA) and (2) blurring is avoided by an integrated model for pathology removal and image reconstruction. Results on synthetic and BRATS 2015 data demonstrate its utility.