Table 4 from How to best combine demosaicing and denoising? | Semantic Scholar (2024)

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It is proved that the best way to reconstruct full color images from a noisy mosaic requires an adaptation of classic denoising algorithms to demosaicked noise, which is justified and specify.

This paper proposes and explores a way to transform CFA data to a form that is amenable to existing grayscale and color denoising schemes, and can expect to reduce processing time and power requirements to about a third of current requirements.

This paper proposes a systematic procedure for estimating ground truth for noisy images that can be used to benchmark denoising performance for smartphone cameras and shows that CNN-based methods perform better when trained on the authors' high-quality dataset than when trained using alternative strategies, such as low-ISO images used as a proxy for ground truth data.

A new data-driven approach forDemosaicking and denoising is introduced: a deep neural network is trained on a large corpus of images instead of using hand-tuned filters and this network and training procedure outperform state-of-the-art both on noisy and noise-free data.

Experimental results demonstrate that the proposed local directional interpolation and nonlocal adaptive thresh- olding method outperforms many state-of-the-art CDM methods in reconstructing the edges and reducing color interpolation artifacts, leading to higher visual quality of reproduced color images.

A principle component analysis (PCA) based spatially-adaptive denoising algorithm, which works directly on the CFA data using a supporting window to analyze the local image statistics, which can effectively suppress noise while preserving color edges and details.

The results demonstrate the effectiveness of the proposed grouping constraint and show that the developed denoising algorithm achieves state-of-the-art performance in terms of both peak signal-to-noise ratio and visual quality.

An algorithm based on an enhanced sparse representation in transform domain based on a specially developed collaborative Wiener filtering achieves state-of-the-art denoising performance in terms of both peak signal-to-noise ratio and subjective visual quality.

A novel color demosaicing algorithm for noisy data that is found to be efficient in order to attenuate both noise and interpolation errors and shows the efficiency of the proposed approach in terms of both numerical and visual evaluation.

The solution, which improves preservation of details in the NR filtering before the CFAI, is proposed, and is based on the quality of the output image, the processing power requirements and the amount of memory needed.

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