Color demosaicking by local directional interpolation and nonlocal adaptive thresholding

By Lei Zhang, Xiaolin Wu, A. Buades, Xin Li. J. Electronic Imaging, 2011

614 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Media Technology

View Paper: https://www.semanticscholar.org/paper/47b9a4dfbff5d2bb8b4f8f6e6fc2294426aab8ac

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In Computer Vision and Pattern Recognition

In Media Technology

Citations in the corpus

FFDNet: Toward a Fast and Flexible Solution for CNN-Based Image Denoising (2017, methodology): “Compared to the Kodak24 images, the images in McMaster dataset exhibit more saturated colors [60].”
Deep Learning on Image Denoising: An overview (2019, neutral): “The color-noisy image dataset included CBSD68, Kodak24 [57], McMaster [263], cc [160], DND [171], NC12 [112], SIDD [3] and Nam [160].”
Efficient and Explicit Modelling of Image Hierarchies for Image Restoration (2023, neutral): “Method # Params [M] Color Grayscale CBSD68 [49] Kodak24 [16] McMaster [83] Urban100 [28] Set12 [82] BSD68 [49] Urban100 [28]”
MambaIR: A Simple Baseline for Image Restoration with State-Space Model (2024, methodology): “Our testing datasets for guassian color image denoising includes BSD68 [50], Kodak24 [17], McMaster [84], and Urban100 [29].”
Attention-guided CNN for image denoising (2020, neutral): “We evaluate the denosing performance of ADNet via six datasets, i.e. r BSD68 [41], Set12, CBSD68 [41], Kodak24 [12], McMaster [60] and cc [37], which consist of 68, 12, 68, 24, 18 and 15 images, respectively.”
Restormer: Efficient Transformer for High-Resolution Image Restoration (2021, methodology): “We perform denoising experiments on synthetic benchmark datasets generated with additive white Gaussian noise (Set12 [101], BSD68 [52], Urban100 [29], Kodak24 [20] and McMaster [104]) as well as on real-world datasets (SIDD [1] and DND [60]).”
Image Restoration with Mean-Reverting Stochastic Differential Equations (2023, methodology): “Then all models are evaluated on the McMaster (Zhang et al., 2011), Ko-dak24 (Franzen, 1999), and CBSD68 (Martin et al., 2001) datasets.”
Image denoising using deep CNN with batch renormalization (2020, neutral): “The test set is McMaster [65].”