Hierarchical Features Driven Residual Learning for Depth Map Super-Resolution

Guo, Chunle; Li, Chongyi; Guo, Jun; Cong, Runmin; Fu, Huazhu; Han, Ping

doi:10.1109/tip.2018.2887029

Cited by 166 publications

(118 citation statements)

References 45 publications

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“…Additionally, it is practically impossible to simultaneously photograph a real underwater scene and the corresponding ground truth image for different water types. Lacking sufficient and effective training data, the performance of deep learningbased underwater image enhancement algorithms does not match the success of recent deep learning-based high-level and low-level vision problems [11]- [15]. To advance the development of underwater image enhancement, we construct a large-scale real-world Underwater Image Enhancement Benchmark (UIEB).…”

Section: Introductionmentioning

confidence: 99%

An Underwater Image Enhancement Benchmark Dataset and Beyond

Guo

Ren

et al. 2020

IEEE Trans. on Image Process.

Self Cite

999

672

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Underwater image enhancement has been attracting much attention due to its significance in marine engineering and aquatic robotics. Numerous underwater image enhancement algorithms have been proposed in the last few years. However, these algorithms are mainly evaluated using either synthetic datasets or few selected real-world images. It is thus unclear how these algorithms would perform on images acquired in the wild and how we could gauge the progress in the field. To bridge this gap, we present the first comprehensive perceptual study and analysis of underwater image enhancement using large-scale real-world images. In this paper, we construct an Underwater Image Enhancement Benchmark (UIEB) including 950 realworld underwater images, 890 of which have the corresponding reference images. We treat the rest 60 underwater images which cannot obtain satisfactory reference images as challenging data. Using this dataset, we conduct a comprehensive study of the stateof-the-art underwater image enhancement algorithms qualitatively and quantitatively. In addition, we propose an underwater image enhancement network (called Water-Net) trained on this benchmark as a baseline, which indicates the generalization of the proposed UIEB for training Convolutional Neural Networks (CNNs). The benchmark evaluations and the proposed Water-Net demonstrate the performance and limitations of state-of-the-art algorithms, which shed light on future research in underwater image enhancement. The dataset and code are available at https://li-chongyi.github.io/proj benchmark.html.

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Section: Introductionmentioning

confidence: 99%

An Underwater Image Enhancement Benchmark Dataset and Beyond

Guo

Ren

et al. 2020

IEEE Trans. on Image Process.

Self Cite

999

672

View full text Add to dashboard Cite

show abstract

“…In recent years, deep learning theory has developed well in the field of image processing, especially in the fields of semantic segmentation [27], object detection [28], and image super-resolution [29]. Some scholars have begun to study how to use Deep Convolutional Neural Network (DCN) [5] for image dehazing.…”

Section: B Image Dehazing Methods Based On Learningmentioning

confidence: 99%

Iterative Residual Network for Image Dehazing

Hua

Fan

2020

IEEE Access

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In this paper, we propose an Iterative Residual Network. By designing the calculation unit, we put the hazy image into the calculation unit to perform an iterative operation that can stitch the hazy image in stages with the unit output and substitute it into the calculation. After multiple iterations, a clean image can be generated. We introduce Long Short-Term Memory network and Residual ideas in the design process of the computing unit to further optimize the model. The Long Short-Term Memory network can be used to connect computing units at different stages. The use of residual block connection in the deep processing of the computing unit can preserve the original features of the image and prevent the model from overfitting. This model directly generates hazy-free images in an end-to-end manner and does not rely on the estimations of the transmission map and atmospheric light. Experiments show that Iterative Residual Network can effectively remove the haze in the image. In the test of the synthetic dataset and the real dataset, Iterative Residual Network is superior to the existing methods in terms of PSNR, SSIM, FADE and subjective visual effects.

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“…They then use a deep CNN to estimate the representation coefficients to restore the target image. For the specific MIR task, methods [28], [29], [30], [31] aim to upscale the depth image with guidance from the RGB image using deep neural networks. Methods [32], [33], [34] aim to improve the resolution of the multi-spectral image with the assistance of either RGB or panchromatic images.…”

Section: Multi-modal Image Restorationmentioning

confidence: 99%

Deep Convolutional Neural Network for Multi-Modal Image Restoration and Fusion

Deng

Dragotti

2021

IEEE Trans. Pattern Anal. Mach. Intell.

118

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In this paper, we propose a novel deep convolutional neural network to solve the general multi-modal image restoration (MIR) and multi-modal image fusion (MIF) problems. Different from other methods based on deep learning, our network architecture is designed by drawing inspirations from a new proposed multi-modal convolutional sparse coding (MCSC) model. The key feature of the proposed network is that it can automatically split the common information shared among different modalities, from the unique information that belongs to each single modality, and is therefore denoted with CU-Net, i.e., Common and Unique information splitting network. Specifically, the CU-Net is composed of three modules, i.e., the unique feature extraction module (UFEM), common feature preservation module (CFPM), and image reconstruction module (IRM). The architecture of each module is derived from the corresponding part in the MCSC model, which consists of several learned convolutional sparse coding (LCSC) blocks. Extensive numerical results verify the effectiveness of our method on a variety of MIR and MIF tasks, including RGB guided depth image super-resolution, flash guided non-flash image denoising, multi-focus and multi-exposure image fusion. Index Terms-Multi-modal image restoration, image fusion, multi-modal convolutional sparse coding ! • The authors are with the

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Hierarchical Features Driven Residual Learning for Depth Map Super-Resolution

Cited by 166 publications

References 45 publications

An Underwater Image Enhancement Benchmark Dataset and Beyond

An Underwater Image Enhancement Benchmark Dataset and Beyond

Iterative Residual Network for Image Dehazing

Deep Convolutional Neural Network for Multi-Modal Image Restoration and Fusion

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