FreqNet: A Frequency-domain Image Super-Resolution Network with Dicrete Cosine Transform

• URL: http://arxiv.org/abs/2111.10800v1
• Date: Sun, 21 Nov 2021 11:49:12 GMT
• Title: FreqNet: A Frequency-domain Image Super-Resolution Network with Dicrete Cosine Transform
• Authors: Runyuan Cai, Yue Ding, Hongtao Lu
• Abstract summary: Single image super-resolution(SISR) is an ill-posed problem that aims to obtain high-resolution (HR) output from low-resolution (LR) input. Despite the high peak signal-to-noise ratios(PSNR) results, it is difficult to determine whether the model correctly adds desired high-frequency details. We propose FreqNet, an intuitive pipeline from the frequency domain perspective, to solve this problem.
• Score: 16.439669339293747
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Single image super-resolution(SISR) is an ill-posed problem that aims to obtain high-resolution (HR) output from low-resolution (LR) input, during which extra high-frequency information is supposed to be added to improve the perceptual quality. Existing SISR works mainly operate in the spatial domain by minimizing the mean squared reconstruction error. Despite the high peak signal-to-noise ratios(PSNR) results, it is difficult to determine whether the model correctly adds desired high-frequency details. Some residual-based structures are proposed to guide the model to focus on high-frequency features implicitly. However, how to verify the fidelity of those artificial details remains a problem since the interpretation from spatial-domain metrics is limited. In this paper, we propose FreqNet, an intuitive pipeline from the frequency domain perspective, to solve this problem. Inspired by existing frequency-domain works, we convert images into discrete cosine transform (DCT) blocks, then reform them to obtain the DCT feature maps, which serve as the input and target of our model. A specialized pipeline is designed, and we further propose a frequency loss function to fit the nature of our frequency-domain task. Our SISR method in the frequency domain can learn the high-frequency information explicitly, provide fidelity and good perceptual quality for the SR images. We further observe that our model can be merged with other spatial super-resolution models to enhance the quality of their original SR output.

Related papers

• WAVE-UNET: Wavelength based Image Reconstruction method using attention UNET for OCT images [1.0835264351334324]
  We propose a systematic design methodology WAVE-UNET to reconstruct the high-quality OCT images directly from the lambda-space to reduce the complexity. This framework uses modified UNET having attention gating and residual connections, with IDFT processed lambda-space fringes as the input. The method consistently outperforms the traditional OCT system by generating good-quality B-scans with highly reduced time-complexity.
  arXiv  Detail & Related papers  (2024-10-05T11:16:10Z)
• FreqINR: Frequency Consistency for Implicit Neural Representation with Adaptive DCT Frequency Loss [5.349799154834945]
  This paper introduces Frequency Consistency for Implicit Neural Representation (FreqINR), an innovative Arbitrary-scale Super-resolution method. During training, we employ Adaptive Discrete Cosine Transform Frequency Loss (ADFL) to minimize the frequency gap between HR and ground-truth images. During inference, we extend the receptive field to preserve spectral coherence between low-resolution (LR) and ground-truth images.
  arXiv  Detail & Related papers  (2024-08-25T03:53:17Z)
• Spatial-Frequency U-Net for Denoising Diffusion Probabilistic Models [89.76587063609806]
  We study the denoising diffusion probabilistic model (DDPM) in wavelet space, instead of pixel space, for visual synthesis. By explicitly modeling the wavelet signals, we find our model is able to generate images with higher quality on several datasets.
  arXiv  Detail & Related papers  (2023-07-27T06:53:16Z)
• A Scale-Arbitrary Image Super-Resolution Network Using Frequency-domain Information [42.55177009667711]
  Image super-resolution (SR) is a technique to recover lost high-frequency information in low-resolution (LR) images. In this paper, we study image features in the frequency domain to design a novel scale-arbitrary image SR network.
  arXiv  Detail & Related papers  (2022-12-08T15:10:49Z)
• Implicit Neural Representation Learning for Hyperspectral Image Super-Resolution [0.0]
  Implicit Neural Representations (INRs) are making strides as a novel and effective representation. We propose a novel HSI reconstruction model based on INR which represents HSI by a continuous function mapping a spatial coordinate to its corresponding spectral radiance values.
  arXiv  Detail & Related papers  (2021-12-20T14:07:54Z)
• Fourier Space Losses for Efficient Perceptual Image Super-Resolution [131.50099891772598]
  We show that it is possible to improve the performance of a recently introduced efficient generator architecture solely with the application of our proposed loss functions. We show that our losses' direct emphasis on the frequencies in Fourier-space significantly boosts the perceptual image quality. The trained generator achieves comparable results with and is 2.4x and 48x faster than state-of-the-art perceptual SR methods RankSRGAN and SRFlow respectively.
  arXiv  Detail & Related papers  (2021-06-01T20:34:52Z)
• Asymmetric CNN for image super-resolution [102.96131810686231]
  Deep convolutional neural networks (CNNs) have been widely applied for low-level vision over the past five years. We propose an asymmetric CNN (ACNet) comprising an asymmetric block (AB), a mem?ory enhancement block (MEB) and a high-frequency feature enhancement block (HFFEB) for image super-resolution. Our ACNet can effectively address single image super-resolution (SISR), blind SISR and blind SISR of blind noise problems.
  arXiv  Detail & Related papers  (2021-03-25T07:10:46Z)
• Learning Frequency-aware Dynamic Network for Efficient Super-Resolution [56.98668484450857]
  This paper explores a novel frequency-aware dynamic network for dividing the input into multiple parts according to its coefficients in the discrete cosine transform (DCT) domain. In practice, the high-frequency part will be processed using expensive operations and the lower-frequency part is assigned with cheap operations to relieve the computation burden. Experiments conducted on benchmark SISR models and datasets show that the frequency-aware dynamic network can be employed for various SISR neural architectures.
  arXiv  Detail & Related papers  (2021-03-15T12:54:26Z)
• Frequency Consistent Adaptation for Real World Super Resolution [64.91914552787668]
  We propose a novel Frequency Consistent Adaptation (FCA) that ensures the frequency domain consistency when applying Super-Resolution (SR) methods to the real scene. We estimate degradation kernels from unsupervised images and generate the corresponding Low-Resolution (LR) images. Based on the domain-consistent LR-HR pairs, we train easy-implemented Convolutional Neural Network (CNN) SR models.
  arXiv  Detail & Related papers  (2020-12-18T08:25:39Z)
• Hyperspectral Image Super-resolution via Deep Progressive Zero-centric Residual Learning [62.52242684874278]
  Cross-modality distribution of spatial and spectral information makes the problem challenging. We propose a novel textitlightweight deep neural network-based framework, namely PZRes-Net. Our framework learns a high resolution and textitzero-centric residual image, which contains high-frequency spatial details of the scene.
  arXiv  Detail & Related papers  (2020-06-18T06:32:11Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.