The Soft Shadow Occlusion Camera

By Qi Mo, V. Popescu, Chris Wyman. Pacific Conference on Computer Graphics and Applications, 2007

416 citations recorded by the source corpus.

Subfields: Computer Graphics and Computer-Aided Design, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/28bb6a0c75888aa517e68c6fd14688448e7a5928

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Graphics and Computer-Aided Design

1. Feng Lu
2. John William Paisley
3. K. Ram Prabhakar
4. R. Venkatesh Babu
5. Cosmin Ancuți
6. Chen Change Loy
7. Chunle Guo
8. Yue Huang

In Computer Vision and Pattern Recognition

1. Jiayi Ma
2. Chongyi Li
3. Chunle Guo
4. Hui Li
5. Xiaojun Wu
6. Yu Liu
7. Runmin Cong
8. Xun Chen

Citations in the corpus

• A medical image fusion method based on convolutional neural networks (2017, methodology): “To address the first problem, we apply a pyramid-based multi-scale approach [27] to pursue perceptually better results.”
• Learning to Enhance Low-Light Image via Zero-Reference Deep Curve Estimation (2021, methodology): “We follow existing practices [30], [31] to set E as the gray level in the RGB color”
• Attention Guided Low-Light Image Enhancement with a Large Scale Low-Light Simulation Dataset (2019, background): “Subsequently, we fuse these differently exposed images following the same routine in [53] (the results called colorful images).”
• Enhancing underwater images and videos by fusion (2012, methodology): “Our approach is built on the fusion principle that has shown utility in several applications such as image compositing [14], multispectral video enhancement [6], defogging [2] and HDR imaging [20].”
• Zero-Reference Deep Curve Estimation for Low-Light Image Enhancement (2020, methodology): “We follow existing practices [19, 20] to setE as the gray level in the RGB color space.”
• DeepFuse: A Deep Unsupervised Approach for Exposure Fusion with Extreme Exposure Image Pairs (2017, methodology): “From the execution times shown in Table 3 we can observe that our method is roughly 3-4 × faster than Mertens”
• A fusion-based enhancing method for weakly illuminated images (2016, methodology): “13(d), the method of [3] has a slight over-enhancement in the sky region, which is not present in the proposed method in Fig.”
• Pixel-level image fusion: A survey of the state of the art (2017, methodology): “[4] have been proposed, which bring opportunities to further improve the performance of image fusion.”