Recently, plenoptic Image has attracted great attentions because of its applications in various scenarios. However, high resolution and special pixel distribution structure bring huge challenges to its storage and tra...
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ISBN:
(纸本)9781728180687
Recently, plenoptic Image has attracted great attentions because of its applications in various scenarios. However, high resolution and special pixel distribution structure bring huge challenges to its storage and transmission. In order to adapt compression to the structural characteristic of plenoptic image, in this paper, we propose a Data Structure Adaptive 3D-convolutional(DSA-3D) autoencoder. The DSA-3D autoencoder enables up-sampling and down-samping the sub-aperture sequence along the angular resolution or spatial resolution, thereby avoiding the artifacts caused by directly compressing plenoptic image and achieving better compression efficiency. In addition, we propose a special and efficient S quare rearrangement to generate sub-aperture sequence. We compare Square with Zigzag sub-aperture sequence rearrangements, and analyzed the compression efficiency of block image compression and whole image compression. Compared with traditional hybrid encoders HEVC, JPEG2000 and JPEG PLENO(WaSP), the proposed DSA-3D(Square) autoencoder achieves a superior performance in terms of PSNR metrics.
Currently, haze removal of images captured at night for foggy scenes rely on the traditional, prior-based methods, but these methods are frequently ineffective at dealing with night hazy images. In addition, the light...
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Currently, haze removal of images captured at night for foggy scenes rely on the traditional, prior-based methods, but these methods are frequently ineffective at dealing with night hazy images. In addition, the light sources at night are complicated and there is a problem of inconsistent brightness. This makes the estimation of the transmission map complicated in the night scene. Based on the above analysis, we propose an autoencoder method to solve the problem of overestimation or underestimation of transmission captured by the traditional, prior-based methods. For nighttime hazy images, we first remove the color effect of the haze image with an edge-preserving maximum reflectance prior (MRP) method. Then, the hazy image without color influence is input into the self-encoder network with skip connections to obtain the transmission map. Moreover, instead of using the local maximum method, we estimate the ambient illumination through a guiding image filtering. In order to highlight the effectiveness of our experiments, a large number of comparison experiments were conducted between our method and the state-of-the-art methods. The results show that our method can effectively suppress the halo effect and reduce the effectiveness of glow. In the experimental part, we calculate that the average Peak Signal to Noise Ratio (PSNR) is 21.0968 and the average Structural Similarity (SSIM) is 0.6802.
autoencoder networks have been successfully applied to dimensionality reduction and information retrieval tasks. Lower-dimensional representations can improve performance on many tasks, such as image compression, reco...
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ISBN:
(纸本)9781643680194;9781643680187
autoencoder networks have been successfully applied to dimensionality reduction and information retrieval tasks. Lower-dimensional representations can improve performance on many tasks, such as image compression, reconstruction and clustering. The Mean Square Error (MSE) is commonly used as a loss function for autoencoder networks. The loss function suffers from performance degradation because it lacks the component distribution information of the input image. In the deep learning literature, recent works have shown the benefits of using adversarialbased losses to improve the performance on various image reconstruction and clustering tasks. This paper proposes a new algorithm which is called GAN-WMSE to generate weights for the MSE based on adversarial networks. With the distribution information integrated into the loss function, the autoencoder network and the adversarial weight network are jointly trained. Experiments on different image datasets show that the improved autoencoder networks employing our loss function can increase performance by 9.8% for PSNR, 10.3% for SSIM in image reconstruction, and 6.2% for image clustering.
The inverse mapping of GANs' (Generative Adversarial Nets) generator has a great potential value. Hence, some works have been developed to construct the inverse function of generator by directly learning or advers...
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ISBN:
(纸本)9783319700960;9783319700953
The inverse mapping of GANs' (Generative Adversarial Nets) generator has a great potential value. Hence, some works have been developed to construct the inverse function of generator by directly learning or adversarial learning. While the results are encouraging, the problem is highly challenging and the existing ways of training inverse models of GANs have many disadvantages, such as hard to train or poor performance. Due to these reasons, we propose a new approach based on using inverse generator (IG) model as encoder and pre-trained generator (G) as decoder of an autoencoder network to train the IG model. In the proposed model, the difference between the input and output, which are both the generated image of pre-trained GAN's generator, of autoencoder is directly minimized. The optimizing method can overcome the difficulty in training and inverse model of an non one-to-one function. We also applied the inverse model of GANs' generators to image searching and translation. The experimental results prove that the proposed approach works better than the traditional approaches in image searching.
Motion trajectory is one of the most important cues for tracking and behavior recognition and can be widely applied to numerous fields. However, it is a difficult problem to directly model the spatio-temporal variatio...
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Motion trajectory is one of the most important cues for tracking and behavior recognition and can be widely applied to numerous fields. However, it is a difficult problem to directly model the spatio-temporal variations of trajectories due to their high dimensionality and nonlinearity. In this paper, we propose a joint trajectory tracking and recognition algorithm by combining a generative model derived from a bi-directional deep. neural network (called "autoencoder") into a Bayesian estimation framework. The "autoencoder" network embeds high-dimensional trajectories into a two-dimensional plane based on a peculiar training rule and learns a trajectory generative model by its inverse mapping. A set of plausible trajectories can be generated by the trajectory generative model. In the tracking process, the samples from the plausible trajectory set are weighted by a mixed likelihood and are resampled to obtain the target state estimation at each time step in spirit of the particle filtering. The trajectory identity is inferred by evaluating the improved Hausdorff distance between the estimated trajectory up to now and the truncated reference trajectories. Moreover, the trajectory recognition results are also used to guide the trajectory tracking for the next time. The experiments on tracking and recognizing handwritten digits show that the proposed approach can achieve both robust tracking and exact recognition in background clutter and partial occlusion. (C) 2008 Elsevier B.V. All rights reserved.
The dentate gyrus is part of the hippocampal memory system and special in that it generates new neurons throughout life. Here we discuss the question of what the functional role of these new neurons might be. Our hypo...
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The dentate gyrus is part of the hippocampal memory system and special in that it generates new neurons throughout life. Here we discuss the question of what the functional role of these new neurons might be. Our hypothesis is that they help the dentate gyrus to avoid the problem of catastrophic interference when adapting to new 14 environments. We assume that old neurons are rather stable and preserve an optimal encoding learned for known environments while new neurons are plastic to adapt to those features that are qualitatively new in a new environment. A simple network simulation demonstrates that adding new plastic neurons is indeed a successful strategy for adaptation without catastrophic interference. (c) 2006 Wiley-Liss, Inc.
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