Biorthogonal wavelets or filterbanks are shown to be superior in coding gain performance than orthogonal ones for logarithmic subband decompositions (limited to iterative decomposition of the downsampled output of the...
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ISBN:
(纸本)081941638X
Biorthogonal wavelets or filterbanks are shown to be superior in coding gain performance than orthogonal ones for logarithmic subband decompositions (limited to iterative decomposition of the downsampled output of the analysis low-pass filter). As a consequence, for logarithmic decompositions, the optimal filter is not an ideal filter. This is shown for maximally regular biorthogonal and orthogonal filters, as well as filters designed to optimize the subband coding grain.
In this work, a method that aims to improve the half tone images hidden by visual cryptography is proposed. visual cryptography produces shared images each of which does not have any hint about the hidden image. When ...
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ISBN:
(纸本)9781467373869
In this work, a method that aims to improve the half tone images hidden by visual cryptography is proposed. visual cryptography produces shared images each of which does not have any hint about the hidden image. When these shared images stacked over one another, the hidden image is revealed without any need of post processing or decoding. The operations that make it impossible to guess the hidden image from a single shared one, also causes the deterioration of the hidden image to a degree. In this work, it is aimed to process the hidden image in a way to reduce the disruption caused by the operations required by visual cryptography. A new method for producing half tone images from gray tone images that is suitable for our aim is introduced and it is shown that how this method produces images that have higher perceptual quality after visual cryptography is applied.
Colorization of near-infrared (NIR) images is a challenging problem due to the different material properties at the infared wavelenghts, thus reducing the correlation with visible images. In this paper, we study how g...
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ISBN:
(纸本)9781728180687
Colorization of near-infrared (NIR) images is a challenging problem due to the different material properties at the infared wavelenghts, thus reducing the correlation with visible images. In this paper, we study how graph-convolutional neural networks allow exploiting a more powerful inductive bias than standard CNNs, in the form of non-local self-similiarity. Its impact is evaluated by showing how training with mean squared error only as loss leads to poor results with a standard CNN, while the graph-convolutional network produces significantly sharper and more realistic colorizations.
Simulation of human visual system (HVS) is very crucial for fitting human perception and improving assessment performance in stereoscopic image quality assessment (SIQA). In this paper, a no-reference SIQA method cons...
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ISBN:
(纸本)9781728185514
Simulation of human visual system (HVS) is very crucial for fitting human perception and improving assessment performance in stereoscopic image quality assessment (SIQA). In this paper, a no-reference SIQA method considering feedback mechanism and orientation selectivity of HVS is proposed. In HVS, feedback connections are indispensable during the process of human perception, which has not been studied in the existing SIQA models. Therefore, we design a new feedback module (FBM) to realize the guidance of the high-level region of visual cortex to the low-level region. In addition, given the orientation selectivity of primary visual cortex cells, a deformable feature extraction block is explored to simulate it, and the block can adaptively select the regions of interest. Meanwhile, retinal ganglion cells (RGCs) with different receptive fields have different sensitivities to objects of different sizes in the image. So a new multi receptive fields information extraction and fusion manner is realized in the network structure. Experimental results show that the proposed model is superior to the state-of-the-art no-reference SIQA methods and has excellent generalization ability.
With the development of deep learning, many methods on image denoising have been proposed processingimages on a fixed scale or multi-scale which is usually implemented by convolution or deconvolution. However, excess...
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ISBN:
(纸本)9781728180687
With the development of deep learning, many methods on image denoising have been proposed processingimages on a fixed scale or multi-scale which is usually implemented by convolution or deconvolution. However, excessive scaling may lose image detail information, and the deeper the convolutional network the easier to lose network gradient. Diamond Denoising Network (DmDN) is proposed in this paper, which mainly based on a fixed scale and meanwhile considering the multi-scale feature information by using the Diamond-Shaped (DS) module to deal with the problems above. Experimental results show that DmDN is effective in image denoising.
The ever higher quality and wide diffusion of fake images have spawn a quest for reliable forensic tools. Many GAN image detectors have been proposed, recently. In real world scenarios, however, most of them show limi...
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ISBN:
(纸本)9781728185514
The ever higher quality and wide diffusion of fake images have spawn a quest for reliable forensic tools. Many GAN image detectors have been proposed, recently. In real world scenarios, however, most of them show limited robustness and generalization ability. Moreover, they often rely on side information not available at test time, that is, they are not universal. We investigate these problems and propose a new GAN image detector based on a limited sub-sampling architecture and a suitable contrastive learning paradigm. Experiments carried out in challenging conditions prove the proposed method to be a first step towards universal GAN image detection, ensuring also good robustness to common image impairments, and good generalization to unseen architectures.
Most approaches in learned image compression follow the transform coding scheme. The characteristics of latent variables transformed from images significantly influence the performance of codecs. In this paper, we pre...
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ISBN:
(纸本)9798331529543;9798331529550
Most approaches in learned image compression follow the transform coding scheme. The characteristics of latent variables transformed from images significantly influence the performance of codecs. In this paper, we present visual analyses on latent features of learned image compression and find that the latent variables are spread over a wide range, which may lead to complex entropy coding processes. To address this, we introduce a Deviation Control (DC) method, which applies a constraint loss on latent features and entropy parameter mu. Training with DC loss, we obtain latent features with smaller values of coding symbols and s, effectively reducing entropy coding complexity. Our experimental results show that the plug-and-play DC loss reduces entropy coding time by 30-40% and improves compression performance.
Frame rate conversion is widely used in transformation among various different video standards. This paper describes an efficient frame rate conversion method using block motion compensation. In this approach, a predi...
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ISBN:
(纸本)081941638X;9780819416384
Frame rate conversion is widely used in transformation among various different video standards. This paper describes an efficient frame rate conversion method using block motion compensation. In this approach, a predicted motion vector and a motion vector preselecting process are invoked. This results in lower complexity, while maintains a higher SNR compared with the existing efficient method.
This paper proposes a new neural network for enhancing underexposed images. Instead of the decomposition method based on Retinex theory, we introduce smooth dilated convolution to estimate global illumination of the i...
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ISBN:
(纸本)9781728180687
This paper proposes a new neural network for enhancing underexposed images. Instead of the decomposition method based on Retinex theory, we introduce smooth dilated convolution to estimate global illumination of the input image, and implement an end-to-end learning network model. Based on this model, we formulate a multi-term loss function that combines content, color, texture and smoothness losses. Our extensive experiments demonstrate that this method is superior to other methods in underexposed image enhancement. It can cover more color details and be applied to various underexposed images robustly.
Biorthogonal wavelets or filterbanks are shown to be superior in coding gain performance than orthogonal ones for logarithmic subband decompositions (limited to iterative decomposition of the downsampled output of the...
详细信息
ISBN:
(纸本)081941638X;9780819416384
Biorthogonal wavelets or filterbanks are shown to be superior in coding gain performance than orthogonal ones for logarithmic subband decompositions (limited to iterative decomposition of the downsampled output of the analysis low-pass filter). As a consequence, for logarithmic decompositions, the optimal filter is not an ideal filter. This is shown for maximally regular biorthogonal and orthogonal filters, as well as filters designed to optimize the subband coding grain.
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