-
AIM 2020 Challenge on Rendering Realistic Bokeh
This paper reviews the second AIM realistic bokeh effect rendering chall...
read it
-
PIRM Challenge on Perceptual Image Enhancement on Smartphones: Report
This paper reviews the first challenge on efficient perceptual image enh...
read it
-
W-Net: Two-stage U-Net with misaligned data for raw-to-RGB mapping
Recent research on a learning mapping between raw Bayer images and RGB i...
read it
-
Replacing Mobile Camera ISP with a Single Deep Learning Model
As the popularity of mobile photography is growing constantly, lots of e...
read it
-
DIFAR: Deep Image Formation and Retouching
We present a novel neural network architecture for the image signal proc...
read it
-
Concise Radiometric Calibration Using The Power of Ranking
Compared with raw images, the more common JPEG images are less useful fo...
read it
-
An Image Processing Pipeline for Automated Packaging Structure Recognition
Dispatching and receiving logistics goods, as well as transportation its...
read it
AIM 2020 Challenge on Learned Image Signal Processing Pipeline
This paper reviews the second AIM learned ISP challenge and provides the description of the proposed solutions and results. The participating teams were solving a real-world RAW-to-RGB mapping problem, where to goal was to map the original low-quality RAW images captured by the Huawei P20 device to the same photos obtained with the Canon 5D DSLR camera. The considered task embraced a number of complex computer vision subtasks, such as image demosaicing, denoising, white balancing, color and contrast correction, demoireing, etc. The target metric used in this challenge combined fidelity scores (PSNR and SSIM) with solutions' perceptual results measured in a user study. The proposed solutions significantly improved the baseline results, defining the state-of-the-art for practical image signal processing pipeline modeling.
READ FULL TEXT
Comments
There are no comments yet.