Wavelet Domain Style Transfer for an Effective Perception-Distortion Tradeoff in Single Image Super-Resolution

Deng, Xin; Yang, Ren; Xu, Mai; Dragotti, Pier Luigi

doi:10.1109/iccv.2019.00317

Cited by 79 publications

(48 citation statements)

References 32 publications

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“…Furthermore,'s' and 'd' are short for 'stride' and 'dilation rate', respectively. Wavelet Channel: The 2D SWT can decompose an image into multiple sub-bands, including one low-frequency sub-band and several high-frequency sub-bands [22]. The low-frequency sub-band has a significant impact on the objective quality of the image, while the high-frequency sub-band has a significant impact on the perceived quality.…”

Section: Network Architecturementioning

confidence: 99%

Deep Dehazing Network for Remote Sensing Image with Non-Uniform Haze

et al. 2021

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The haze in remote sensing images can cause the decline of image quality and bring many obstacles to the applications of remote sensing images. Considering the non-uniform distribution of haze in remote sensing images, we propose a single remote sensing image dehazing method based on the encoder–decoder architecture, which combines both wavelet transform and deep learning technology. To address the clarity issue of remote sensing images with non-uniform haze, we preliminary process the input image by the dehazing method based on the atmospheric scattering model, and extract the first-order low-frequency sub-band information of its 2D stationary wavelet transform as an additional channel. Meanwhile, we establish a large-scale hazy remote sensing image dataset to train and test the proposed method. Extensive experiments show that the proposed method obtains greater advantages over typical traditional methods and deep learning methods qualitatively. For the quantitative aspects, we take the average of four typical deep learning methods with superior performance as a comparison object using 500 random test images, and the peak-signal-to-noise ratio (PSNR) value using the proposed method is improved by 3.5029 dB, and the structural similarity (SSIM) value is improved by 0.0295, respectively. Based on the above, the effectiveness of the proposed method for the problem of remote sensing non-uniform dehazing is verified comprehensively.

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Section: Network Architecturementioning

confidence: 99%

Deep Dehazing Network for Remote Sensing Image with Non-Uniform Haze

et al. 2021

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“…Provenzi et al (2014) design a wavelet-based perceptually inspired color correction variational model to deal with both the color enhancement and color cast removal. To increase the high-frequency information, several methods (Huang et al 2017;Zhong et al 2018;Huang et al 2019;Deng et al 2019) exploit wavelet transform in the network architecture and improve the performance of super-resolution. Recently, a few works have explored the application of wavelet transform in image deraining.…”

Section: Wavelet Domain Image Synthesismentioning

confidence: 99%

Selective Wavelet Attention Learning for Single Image Deraining

Huang

Chai³

et al. 2021

Int J Comput Vis

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Single image deraining refers to the process of restoring the clean background scene from a rainy image. Current approaches have resorted to deep learning techniques to remove rain from a single image by leveraging some prior information. However, due to the various appearances of rain streaks and accumulation, it is difficult to separate rain and background information in the embedding space, which results in inaccurate deraining. To address this issue, this paper proposes a selective wavelet attention learning method by learning a series of wavelet attention maps to guide the separation of rain and background information in both spatial and frequency domains. The key aspect of our method is utilizing wavelet transform to learn the content and structure of rainy features because the high-frequency features are more sensitive to rain degradations, whereas the low-frequency features preserve more of the background content. To begin with, we develop a selective wavelet attention encoder-decoder network to learn wavelet attention maps guiding the separation of rainy and background features at multiple scales. Meanwhile, we introduce wavelet pooling and unpooling to the encoder-decoder network, which shows superiority in learning increasingly abstract representations while preserving the background details. In addition, we propose latent alignment learning to supervise the background features as well as augment the training data to further improve the accuracy of deraining. Finally, we employ a hierarchical discriminator network based on selective wavelet attention to adversarially improve the visual fidelity of the generated results both globally and locally. Extensive experiments on synthetic and real datasets demonstrate that the proposed approach achieves more appealing results both quantitatively and qualitatively than the recent state-of-the-art methods. Keywords Deraining • Wavelet transform • Selective attention • Wavelet pooling and unpooling Communicated by Dengxin Dai.

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“…Generative adversarial network (GAN) based SISR models, such as SRGAN [9] and ESRGAN [17], are introduced to address this issue, and enhances the images in a photo-realistic way. Enhancing the image to both quantitatively accurate and perceptually realistic within the perception-distortion tradeoff [1] is now an important issue in image enhancement tasks [2].…”

Section: Related Workmentioning

confidence: 99%

PyNET-CA: Enhanced PyNET with Channel Attention for End-to-End Mobile Image Signal Processing

Kim

Song

et al. 2020

Computer Vision – ECCV 2020 Workshops

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Reconstructing RGB image from RAW data obtained with a mobile device is related to a number of image signal processing (ISP) tasks, such as demosaicing, denoising, etc. Deep neural networks have shown promising results over hand-crafted ISP algorithms on solving these tasks separately, or even replacing the whole reconstruction process with one model. Here, we propose PyNET-CA, an end-to-end mobile ISP deep learning algorithm for RAW to RGB reconstruction. The model enhances PyNET, a recently proposed state-of-the-art model for mobile ISP, and improve its performance with channel attention and subpixel reconstruction module. We demonstrate the performance of the proposed method with comparative experiments and results from the AIM 2020 learned smartphone ISP challenge. The source code of our implementation is available at https://github.com/egyptdj/skyb-aim2020-public

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Wavelet Domain Style Transfer for an Effective Perception-Distortion Tradeoff in Single Image Super-Resolution

Cited by 79 publications

References 32 publications

Deep Dehazing Network for Remote Sensing Image with Non-Uniform Haze

Deep Dehazing Network for Remote Sensing Image with Non-Uniform Haze

Selective Wavelet Attention Learning for Single Image Deraining

PyNET-CA: Enhanced PyNET with Channel Attention for End-to-End Mobile Image Signal Processing

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