Denoising is the task of removing noise from an image.
( Image credit: Beyond a Gaussian Denoiser )
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Whereas adversarial training is employed as the main defence strategy against specific adversarial samples, it has limited generalization capability and incurs excessive time complexity.
The performance of objective image quality assessment (IQA) models has been evaluated primarily by comparing model predictions to human judgments.
Instance-level denoising on the feature map is performed to enhance the detection to small and cluttered objects.
Furthermore, the evaluation in terms of quantitative metrics and visual quality for four restoration tasks i. e. Denoising, Super-resolution, Raindrop Removal, and JPEG Compression on 11 real degraded datasets against more than 30 state-of-the-art algorithms demonstrate the superiority of our R$^2$Net.
Furthermore, those tests illustrate that the proposed method is able to adaptively control the global image brightness according to the content of the image scene.
We create a synthetic dataset from a corpus of user reviews by sampling a review, pretending it is a summary, and generating noisy versions thereof which we treat as pseudo-review input.
Empirical evaluation on five different tasks shows that (1) our algorithm is more accurate than several existing methods of learning from a mix of clean and noisy supervision, and (2) the coupled rule-exemplar supervision is effective in denoising rules.