Image Denoising with Control over Deep Network Hallucination

Liang, Qiyuan; Cassayre, Florian; Owsianko, Haley; Helou, Majed El; Süsstrunk, Sabine

doi:10.48550/arxiv.2201.00429

Cited by 1 publication

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“…However, it does not consider signal autocorrelation, making it difficult to estimate the real-world noise in some forms. In 2022, for the enhanced control and interpretability of a learningbased denoiser, Liang et al proposed a novel architecture based on a denoising network called controllable confidence-based image denoising (CCID) [23]. CCID merges the deep denoising network with a reliable filter to reduce artifacts.…”

Section: Introductionmentioning

confidence: 99%

An Unsupervised Weight Map Generative Network for Pixel-Level Combination of Image Denoisers

Luo

et al. 2022

Applied Sciences

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Image denoising is a classic but still important issue in image processing as the denoising effect has a significant impact on subsequent image processing results, such as target recognition and edge detection. In the past few decades, various denoising methods have been proposed, such as model-based and learning-based methods, and they have achieved promising results. However, no stand-alone method consistently outperforms the others in different complex imaging situations. Based on the complementary strengths of model-based and learning-based methods, in this study, we design a pixel-level image combination strategy to leverage their respective advantages for the denoised images (referred to as initial denoised images) generated by individual denoisers. The key to this combination strategy is to generate a corresponding weight map of the same size for each initial denoised image. To this end, we introduce an unsupervised weight map generative network that adjusts its parameters to generate a weight map for each initial denoised image under the guidance of our designed loss function. Using the weight maps, we are able to fully utilize the internal and external information of various denoising methods at a finer granularity, ensuring that the final combined image is close to the optimal. To the best of our knowledge, our enhancement method of combining denoised images at the pixel level is the first proposed in the image combination field. Extensive experiments demonstrate that the proposed method shows superior performance, both quantitatively and visually, and stronger generalization. Specifically, in comparison with the stand-alone denoising methods FFDNet and BM3D, our method improves the average peak signal-to-noise ratio (PSNR) by 0.18 dB to 0.83 dB on two benchmarking datasets crossing different noise levels. Its denoising effect is also greater than other competitive stand-alone methods and combination methods, and has surpassed the denoising effect of the second-best method by 0.03 dB to 1.42 dB. It should be noted that since our image combination strategy is generic, the proposed combined strategy can not only be used for image denoising but can also be extended to low-light image enhancement, image deblurring or image super-resolution.

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Section: Introductionmentioning

confidence: 99%

An Unsupervised Weight Map Generative Network for Pixel-Level Combination of Image Denoisers

Luo

et al. 2022

Applied Sciences

View full text Add to dashboard Cite

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Image Denoising with Control over Deep Network Hallucination

Cited by 1 publication

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An Unsupervised Weight Map Generative Network for Pixel-Level Combination of Image Denoisers

An Unsupervised Weight Map Generative Network for Pixel-Level Combination of Image Denoisers

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