Learning photographic global tonal adjustment with a database of input/output image pairs
By Vladimir Bychkovsky, Sylvain Paris, Eric Chan, Frédo Durand. 2011
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
- Salman Hameed Khan
- Syed Waqas Zamir
- Fahad Shahbaz Khan
- Ming-Hsuan Yang
- Munawar Hayat
- Ling Shao
- Aditya Arora
- Chunhua Shen
Citations in the corpus
- Memory-Efficient Hierarchical Neural Architecture Search for Image Denoising (2019, methodology): “g dataset SIM1800, which contains 1600 training samples and 212 test samples. Firstly, we use the camera pipeline simulation method to add noises to 25k patches extracted from the MIT-Adobe5k dataset [1]. We then manually pick 1812 patches which have the most realistic visual effects and finally rand”
- A Comprehensive Survey of Deep Learning for Image Captioning (2018, methodology): “ngeofentitiessuchascelebritiesandlandmarks. Aseriesofhumanjudgments are applied for evaluating the performances of generated captions. In experiments, it uses three datasets: MS COCO, Adobe-MIT FiveK [19], and images from Instagram. The images of MS COCO dataset were collected from the same domain b”
- Methods for image denoising using convolutional neural network: a review (2021, methodology): “They include: ImageNet large scale visual recognition challenge object detection (ILSVRCDET) [122], Places2 [123], BerkeleySegmentation Dataset (BSD) [124], waterloo explorationdatabase [125], EMNIST [126], COCO dataset [127], MIT-Adobe five k [128], ImageNet [129], BSD68 [130], Set 14 [131], Renoir”
- Learning Enriched Features for Real Image Restoration and Enhancement (2020, methodology): “We report PSNR/SSIM values of our method and several other techniques in Table 5 and Table 6 for the LoL [107] and MITAdobe FiveK [12] datasets, respectively.”
- Toward Fast, Flexible, and Robust Low-Light Image Enhancement (2022, unknown): “As for low-light image enhancement, we randomly sampled 100 images from MIT dataset [2] and 50 testing image from LSRW dataset [9] for testing.”
- Sequential line search for efficient visual design optimization by crowds (2017, methodology): “Photo color enhancement requires adjusting multiple parameters while considering the image contents [Bychkovsky et al. 2011; Koyama et al. 2016].”