Retouche numérique

Résumé

La retouche numérique est le processus d'altération délibérée d', qu'elles soient des photographies ou d'autres pictogrammes créés par ordinateur. La manipulation se fait au moyen de logiciels spécialisés dans les ou . Voir aussi *

Trucage photographique

Source officielle

https://fr.wikipedia.org/wiki/Retouche_num%C3%A9rique

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Publications associées (44)

Resolution-robust Large Mask In painting with Fourier Convolutions

Anastasia Remizova

Modern image inpainting systems, despite the significant progress, often struggle with large missing areas, complex geometric structures, and high-resolution images. We find that one of the main reasons for that is the lack of an effective receptive field in both the inpainting network and the loss function. To alleviate this issue, we propose a new method called large mask inpainting (LaMa). LaMa is based on i) a new inpainting network architecture that uses fast Fourier convolutions (FFCs), which have the imagewide receptive field; ii) a high receptive field perceptual loss; iii) large training masks, which unlocks the potential of the first two components. Our inpainting network improves the state-of-the-art across a range of datasets and achieves excellent performance even in challenging scenarios, e.g. completion of periodic structures. Our model generalizes surprisingly well to resolutions that are higher than those seen at train time, and achieves this at lower parameter&time costs than the competitive baselines. The code is available at https://github.com/saic-mdal/lama.

IEEE COMPUTER SOC2022

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An FPGA Based Practical Implementation of Stochastic Resonance For Image Enhancement

Sumit Kumar

ICLAB2019

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Optimizing Latent Space Directions For GAN-based Local Image Editing

Ehsan Pajouheshgar, Sabine Süsstrunk, Tong Zhang

Generative Adversarial Network (GAN) based localized image editing can suffer from ambiguity between semantic attributes. We thus present a novel objective function to evaluate the locality of an image edit. By introducing the supervision from a pre-trained segmentation network and optimizing the objective function, our framework, called Locally Effective Latent Space Direction (LELSD), is applicable to any dataset and GAN architecture. Our method is also computationally fast and exhibits a high extent of disentanglement, which allows users to interactively perform a sequence of edits on an image. Our experiments on both GAN-generated and real images qualitatively demonstrate the high quality and advantages of our method.

IEEE2021

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