mlsanityData to 7 Oct 2026

DSLR-Quality Photos on Mobile Devices with Deep Convolutional Networks

arXiv 1704.02470 ↗First posted 8 Apr 2017cs.CVcited by 156

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Abstract (arXiv)

Despite a rapid rise in the quality of built-in smartphone cameras, their physical limitations - small sensor size, compact lenses and the lack of specific hardware, - impede them to achieve the quality results of DSLR cameras. In this work we present an end-to-end deep learning approach that bridges this gap by translating ordinary photos into DSLR-quality images. We propose learning the translation function using a residual convolutional neural network that improves both color rendition and image sharpness. Since the standard mean squared loss is not well suited for measuring perceptual image quality, we introduce a composite perceptual error function that combines content, color and texture losses. The first two losses are defined analytically, while the texture loss is learned in an adversarial fashion. We also present DPED, a large-scale dataset that consists of real photos captured from three different phones and one high-end reflex camera. Our quantitative and qualitative assessments reveal that the enhanced image quality is comparable to that of DSLR-taken photos, while the methodology is generalized to any type of digital camera.

Key facts. Cited by 156 papers in the map (data through 7 Oct 2026). First posted on arXiv 8 Apr 2017.

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Citing papers per year: 2021: 39; 2022: 33; 2023: 27; 2024: 30; 2025: 21; 2026: 6.

156 papers.

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  10. 2022Real-World Blind Super-Resolution via Feature Matching with Implicit High-Resolution Priorscited by 98
  11. 2022Efficient and Degradation-Adaptive Network for Real-World Image Super-Resolutioncited by 96
  12. 2021Blind Image Super-Resolution: A Survey and Beyondcited by 93
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  3. 2021PDC-Net+: Enhanced Probabilistic Dense Correspondence Networkcited by 80
  4. 2022CiaoSR: Continuous Implicit Attention-in-Attention Network for Arbitrary-Scale Image Super-Resolutioncited by 69
  5. 2023Residual Denoising Diffusion Modelscited by 68
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  7. 2021Burst Image Restoration and Enhancementcited by 64
  8. 2023FourLLIE: Boosting Low-Light Image Enhancement by Fourier Frequency Informationcited by 64
  9. 2024Efficient Diffusion Model for Image Restoration by Residual Shiftingcited by 64
  10. 2021PPR10K: A Large-Scale Portrait Photo Retouching Dataset with Human-Region Mask and Group-Level Consistencycited by 59
  11. 2024Towards Realistic Data Generation for Real-World Super-Resolutioncited by 56
  12. 2021Adaptive Unfolding Total Variation Network for Low-Light Image Enhancementcited by 55
  13. 2023PUGAN: Physical Model-Guided Underwater Image Enhancement Using GAN with Dual-Discriminatorscited by 49
  14. 2024Towards Robust Event-guided Low-Light Image Enhancement: A Large-Scale Real-World Event-Image Dataset and Novel Approachcited by 48
  15. 2021Deep Model Intellectual Property Protection via Deep Watermarkingcited by 47
  16. 2021Learning RAW-to-sRGB Mappings with Inaccurately Aligned Supervisioncited by 43
  17. 2021Warp Consistency for Unsupervised Learning of Dense Correspondencescited by 42
  18. 2023Lighting up NeRF via Unsupervised Decomposition and Enhancementcited by 40
  19. 2021Learned Smartphone ISP on Mobile NPUs with Deep Learning, Mobile AI 2021 Challenge: Reportcited by 34
  20. 2021Real-Time Quantized Image Super-Resolution on Mobile NPUs, Mobile AI 2021 Challenge: Reportcited by 34
  21. 20224D LUT: Learnable Context-Aware 4D Lookup Table for Image Enhancementcited by 34
  22. 2023Towards Unified Deep Image Deraining: A Survey and A New Benchmarkcited by 34
  23. 2021Semantically Contrastive Learning for Low-light Image Enhancementcited by 31
  24. 2021DeFlow: Learning Complex Image Degradations from Unpaired Data with Conditional Flowscited by 30
  25. 2021Blind Image Super-resolution with Elaborate Degradation Modeling on Noise and Kernelcited by 27
  26. 2024SeD: Semantic-Aware Discriminator for Image Super-Resolutioncited by 26
  27. 2024You Only Need One Step: Fast Super-Resolution with Stable Diffusion via Scale Distillationcited by 23
  28. 2021Beyond Joint Demosaicking and Denoising: An Image Processing Pipeline for a Pixel-bin Image Sensorcited by 22
  29. 2022Learning to Adapt to Lightcited by 21
  30. 2023Diffusion Model for Dense Matchingcited by 21
  31. 2021Fast and Accurate Single-Image Depth Estimation on Mobile Devices, Mobile AI 2021 Challenge: Reportcited by 19
  32. 2021Real-Time Video Super-Resolution on Smartphones with Deep Learning, Mobile AI 2021 Challenge: Reportcited by 19
  33. 2022KXNet: A Model-Driven Deep Neural Network for Blind Super-Resolutioncited by 18
  34. 2022Rawgment: Noise-Accounted RAW Augmentation Enables Recognition in a Wide Variety of Environmentscited by 18
  35. 2023Optical Aberration Correction in Postprocessing using Imaging Simulationcited by 18
  36. 2023Efficient Test-Time Adaptation for Super-Resolution with Second-Order Degradation and Reconstructioncited by 18
  37. 2024Unleashing the Power of One-Step Diffusion based Image Super-Resolution via a Large-Scale Diffusion Discriminatorcited by 18
  38. 2021Training a Task-Specific Image Reconstruction Losscited by 16

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