33,377 research outputs found
Learned Variable-Rate Image Compression with Residual Divisive Normalization
Recently it has been shown that deep learning-based image compression has
shown the potential to outperform traditional codecs. However, most existing
methods train multiple networks for multiple bit rates, which increases the
implementation complexity. In this paper, we propose a variable-rate image
compression framework, which employs more Generalized Divisive Normalization
(GDN) layers than previous GDN-based methods. Novel GDN-based residual
sub-networks are also developed in the encoder and decoder networks. Our scheme
also uses a stochastic rounding-based scalable quantization. To further improve
the performance, we encode the residual between the input and the reconstructed
image from the decoder network as an enhancement layer. To enable a single
model to operate with different bit rates and to learn multi-rate image
features, a new objective function is introduced. Experimental results show
that the proposed framework trained with variable-rate objective function
outperforms all standard codecs such as H.265/HEVC-based BPG and
state-of-the-art learning-based variable-rate methods.Comment: 6 pages, 5 figure
HDRfeat: A Feature-Rich Network for High Dynamic Range Image Reconstruction
A major challenge for high dynamic range (HDR) image reconstruction from
multi-exposed low dynamic range (LDR) images, especially with dynamic scenes,
is the extraction and merging of relevant contextual features in order to
suppress any ghosting and blurring artifacts from moving objects. To tackle
this, in this work we propose a novel network for HDR reconstruction with deep
and rich feature extraction layers, including residual attention blocks with
sequential channel and spatial attention. For the compression of the
rich-features to the HDR domain, a residual feature distillation block (RFDB)
based architecture is adopted. In contrast to earlier deep-learning methods for
HDR, the above contributions shift focus from merging/compression to feature
extraction, the added value of which we demonstrate with ablation experiments.
We present qualitative and quantitative comparisons on a public benchmark
dataset, showing that our proposed method outperforms the state-of-the-art.Comment: 4 pages, 5 figure
Deep generative adversarial residual convolutional networks for real-world super-resolution
Most current deep learning based single image super-resolution (SISR) methods focus on designing deeper / wider models to learn the non-linear mapping between low-resolution (LR) inputs and the high-resolution (HR) outputs from a large number of paired (LR/HR) training data. They usually take as assumption that the LR image is a bicubic down-sampled version of the HR image. However, such degradation process is not available in real-world settings i.e. inherent sensor noise, stochastic noise, compression artifacts, possible mismatch between image degradation process and camera device. It reduces significantly the performance of current SISR methods due to real-world image corruptions. To address these problems, we propose a deep Super-Resolution Residual Convolutional Generative Adversarial Network (SRResCGAN1) to follow the real-world degradation settings by adversarial training the model with pixel-wise supervision in the HR domain from its generated LR counterpart. The proposed network exploits the residual learning by minimizing the energy-based objective function with powerful image regularization and convex optimization techniques. We demonstrate our proposed approach in quantitative and qualitative experiments that generalize robustly to real input and it is easy to deploy for other downscaling operators and mobile/embedded devices
Cascade Decoders-Based Autoencoders for Image Reconstruction
Autoencoders are composed of coding and decoding units, hence they hold the
inherent potential of high-performance data compression and signal compressed
sensing. The main disadvantages of current autoencoders comprise the following
several aspects: the research objective is not data reconstruction but feature
representation; the performance evaluation of data recovery is neglected; it is
hard to achieve lossless data reconstruction by pure autoencoders, even by pure
deep learning. This paper aims for image reconstruction of autoencoders,
employs cascade decoders-based autoencoders, perfects the performance of image
reconstruction, approaches gradually lossless image recovery, and provides
solid theory and application basis for autoencoders-based image compression and
compressed sensing. The proposed serial decoders-based autoencoders include the
architectures of multi-level decoders and the related optimization algorithms.
The cascade decoders consist of general decoders, residual decoders,
adversarial decoders and their combinations. It is evaluated by the
experimental results that the proposed autoencoders outperform the classical
autoencoders in the performance of image reconstruction
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