mlsanityData to 7 Oct 2026

Single Image Super-Resolution via a Dual Interactive Implicit Neural Network

arXiv 2210.12593 ↗First posted 23 Oct 2022cs.CV cs.ROcited by 15

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Abstract (arXiv)

In this paper, we introduce a novel implicit neural network for the task of single image super-resolution at arbitrary scale factors. To do this, we represent an image as a decoding function that maps locations in the image along with their associated features to their reciprocal pixel attributes. Since the pixel locations are continuous in this representation, our method can refer to any location in an image of varying resolution. To retrieve an image of a particular resolution, we apply a decoding function to a grid of locations each of which refers to the center of a pixel in the output image. In contrast to other techniques, our dual interactive neural network decouples content and positional features. As a result, we obtain a fully implicit representation of the image that solves the super-resolution problem at (real-valued) elective scales using a single model. We demonstrate the efficacy and flexibility of our approach against the state of the art on publicly available benchmark datasets.

Key facts. Cited by 15 papers in the map (data through 7 Oct 2026). Builds on 28 of its 39 references that are in the map. First posted on arXiv 23 Oct 2022.

Builds on

28 of 39 references.

References · 28

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  6. 2020Implicit Neural Representations with Periodic Activation Functionscited by 2,214
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16 more
  1. 2015Accurate Image Super-Resolution Using Very Deep Convolutional Networkscited by 902
  2. 2018Residual Dense Network for Image Super-Resolutioncited by 796
  3. 2020Convolutional Occupancy Networkscited by 760
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  10. 2015Deeply-Recursive Convolutional Network for Image Super-Resolutioncited by 317
  11. 2020Neural Unsigned Distance Fields for Implicit Function Learningcited by 265
  12. 2018Deep Back-Projection Networks For Super-Resolutioncited by 224
  13. 2019Meta-SR: A Magnification-Arbitrary Network for Super-Resolutioncited by 180
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  16. 2020X-Fields: Implicit Neural View-, Light- and Time-Image Interpolationcited by 31

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 3; 2024: 6; 2025: 5; 2026: 1.

15 papers.

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  1. 2024GaussianImage: 1000 FPS Image Representation and Compression by 2D Gaussian Splattingcited by 97
  2. 2023Continuous Remote Sensing Image Super-Resolution based on Context Interaction in Implicit Function Spacecited by 17
  3. 2024SketchINR: A First Look into Sketches as Implicit Neural Representationscited by 13
  4. 2024Fourier-enhanced Implicit Neural Fusion Network for Multispectral and Hyperspectral Image Fusioncited by 10
  5. 2024Towards Croppable Implicit Neural Representationscited by 5
  6. 2025Cooperative Decentralized Backdoor Attacks on Vertical Federated Learningcited by 3
  7. 2025Fast Omni-Directional Image Super-Resolution: Adapting the Implicit Image Function with Pixel and Semantic-Wise Spherical Geometric Priorscited by 3
  8. 2023Dual Arbitrary Scale Super-Resolution for Multi-Contrast MRIcited by 1
  9. 2026Self-Supervised Image Super-Resolution Quality Assessment based on Content-Free Multi-Model Oriented Representation Learningcited by 1
  10. 2023A Unified Framework to Super-Resolve Face Images of Varied Low Resolutionscited by 0
  11. 2024MS-Glance: Bio-Insipred Non-semantic Context Vectors and their Applications in Supervising Image Reconstructioncited by 0
  12. 2024Multi-Exposure Image Fusion via Distilled 3D LUT Grid with Editable Modecited by 0
3 more
  1. 2025SBS: Enhancing Parameter-Efficiency of Neural Representations for Neural Networks via Spectral Bias Suppressioncited by 0
  2. 2025Segmentation-Aware Latent Diffusion for Satellite Image Super-Resolution: Enabling Smallholder Farm Boundary Delineationcited by 0
  3. 2025Arbitrary-Resolution and Arbitrary-Scale Face Super-Resolution with Implicit Representation Networkscited by 0