Deep Generative Adversarial Compression Artifact Removal
01 Oct 2017-pp 4836-4845
TL;DR: In this article, a generative adversarial network (GAN) is proposed to produce more photorealistic details than MSE or SSIM-based networks, which can be used as a pre-processing step for object detection in case images are degraded by compression to a point that state-of-the art detectors fail.
Abstract: Compression artifacts arise in images whenever a lossy compression algorithm is applied. These artifacts eliminate details present in the original image, or add noise and small structures; because of these effects they make images less pleasant for the human eye, and may also lead to decreased performance of computer vision algorithms such as object detectors. To eliminate such artifacts, when decompressing an image, it is required to recover the original image from a disturbed version. To this end, we present a feed-forward fully convolutional residual network model trained using a generative adversarial framework. To provide a baseline, we show that our model can be also trained optimizing the Structural Similarity (SSIM), which is a better loss with respect to the simpler Mean Squared Error (MSE). Our GAN is able to produce images with more photorealistic details than MSE or SSIM based networks. Moreover we show that our approach can be used as a pre-processing step for object detection in case images are degraded by compression to a point that state-of-the art detectors fail. In this task, our GAN method obtains better performance than MSE or SSIM trained networks.
Citations
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01 Oct 2019
TL;DR: In this article, an encoder, decoder/generator and a multi-scale discriminator are trained jointly for a generative learned compression objective, obtaining visually pleasing results at bitrates where previous methods fail and show strong artifacts.
Abstract: We present a learned image compression system based on GANs, operating at extremely low bitrates. Our proposed framework combines an encoder, decoder/generator and a multi-scale discriminator, which we train jointly for a generative learned compression objective. The model synthesizes details it cannot afford to store, obtaining visually pleasing results at bitrates where previous methods fail and show strong artifacts. Furthermore, if a semantic label map of the original image is available, our method can fully synthesize unimportant regions in the decoded image such as streets and trees from the label map, proportionally reducing the storage cost. A user study confirms that for low bitrates, our approach is preferred to state-of-the-art methods, even when they use more than double the bits.
299 citations
Posted Content•
TL;DR: This work extensively study how to combine Generative Adversarial Networks and learned compression to obtain a state-of-the-art generative lossy compression system and bridges the gap between rate-distortion-perception theory and practice.
Abstract: We extensively study how to combine Generative Adversarial Networks and learned compression to obtain a state-of-the-art generative lossy compression system In particular, we investigate normalization layers, generator and discriminator architectures, training strategies, as well as perceptual losses In contrast to previous work, i) we obtain visually pleasing reconstructions that are perceptually similar to the input, ii) we operate in a broad range of bitrates, and iii) our approach can be applied to high-resolution images We bridge the gap between rate-distortion-perception theory and practice by evaluating our approach both quantitatively with various perceptual metrics, and with a user study The study shows that our method is preferred to previous approaches even if they use more than 2x the bitrate
154 citations
Posted Content•
TL;DR: This work proposes GFP-GAN that leverages rich and diverse priors encapsulated in a pretrained face GAN for blind face restoration that achieves superior performance to prior art on both synthetic and real-world datasets.
Abstract: Blind face restoration usually relies on facial priors, such as facial geometry prior or reference prior, to restore realistic and faithful details. However, very low-quality inputs cannot offer accurate geometric prior while high-quality references are inaccessible, limiting the applicability in real-world scenarios. In this work, we propose GFP-GAN that leverages rich and diverse priors encapsulated in a pretrained face GAN for blind face restoration. This Generative Facial Prior (GFP) is incorporated into the face restoration process via novel channel-split spatial feature transform layers, which allow our method to achieve a good balance of realness and fidelity. Thanks to the powerful generative facial prior and delicate designs, our GFP-GAN could jointly restore facial details and enhance colors with just a single forward pass, while GAN inversion methods require expensive image-specific optimization at inference. Extensive experiments show that our method achieves superior performance to prior art on both synthetic and real-world datasets.
152 citations
01 Oct 2019
TL;DR: Wang et al. as discussed by the authors proposed a two-stage, end-to-end method using hidden rPPG information enhancement and attention networks, which is the first attempt to counter video compression loss and recover rPGP signals from highly compressed videos.
Abstract: Remote photoplethysmography (rPPG), which aims at measuring heart activities without any contact, has great potential in many applications (e.g., remote healthcare). Existing rPPG approaches rely on analyzing very fine details of facial videos, which are prone to be affected by video compression. Here we propose a two-stage, end-to-end method using hidden rPPG information enhancement and attention networks, which is the first attempt to counter video compression loss and recover rPPG signals from highly compressed videos. The method includes two parts: 1) a Spatio-Temporal Video Enhancement Network (STVEN) for video enhancement, and 2) an rPPG network (rPPGNet) for rPPG signal recovery. The rPPGNet can work on its own for robust rPPG measurement, and the STVEN network can be added and jointly trained to further boost the performance especially on highly compressed videos. Comprehensive experiments are performed on two benchmark datasets to show that, 1) the proposed method not only achieves superior performance on compressed videos with high-quality videos pair, 2) it also generalizes well on novel data with only compressed videos available, which implies the promising potential for real-world applications.
119 citations
08 Sep 2018
TL;DR: This paper model the video artifact reduction task as aKalman filtering procedure and restore decoded frames through a deep Kalman filtering network and builds a recursive filtering scheme based on the Kalman model.
Abstract: When lossy video compression algorithms are applied, compression artifacts often appear in videos, making decoded videos unpleasant for human visual systems. In this paper, we model the video artifact reduction task as a Kalman filtering procedure and restore decoded frames through a deep Kalman filtering network. Different from the existing works using the noisy previous decoded frames as temporal information in the restoration problem, we utilize the less noisy previous restored frame and build a recursive filtering scheme based on the Kalman model. This strategy can provide more accurate and consistent temporal information, which produces higher quality restoration results. In addition, the strong prior information of prediction residual is also exploited for restoration through a well designed neural network. These two components are combined under the Kalman framework and optimized through the deep Kalman filtering network. Our approach can well bridge the gap between the model-based methods and learning-based methods by integrating the recursive nature of the Kalman model and highly non-linear transformation ability of deep neural network. Experimental results on the benchmark dataset demonstrate the effectiveness of our proposed method.
105 citations
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