617 citations · 721 across the 9 of their papers we have counts for
14 papers
Distilling Style from Image Pairs for Global Forward and Inverse Tone Mapping
Aamir Mustafa, Param Hanji, Rafal K. Mantiuk
Many image enhancement or editing operations, such as forward and inverse tone mapping or color grading, do not have a unique solution, but instead a range of solutions, each repre…
Disentangling Domain and Content
Dan Andrei Iliescu, Aliaksei Mikhailiuk, Damon Wischik +1
Many real-world datasets can be divided into groups according to certain salient features (e.g. grouping images by subject, grouping text by font, etc.). Often, machine learning ta…
How to cheat with metrics in single-image HDR reconstruction
Gabriel Eilertsen, Saghi Hajisharif, Param Hanji +3
Single-image high dynamic range (SI-HDR) reconstruction has recently emerged as a problem well-suited for deep learning methods. Each successive technique demonstrates an improveme…
Training a Task-Specific Image Reconstruction Loss
Aamir Mustafa, Aliaksei Mikhailiuk, Dan Andrei Iliescu +2
The choice of a loss function is an important factor when training neural networks for image restoration problems, such as single image super resolution. The loss function should e…
Consolidated Dataset and Metrics for High-Dynamic-Range Image Quality
Aliaksei Mikhailiuk, Maria Perez-Ortiz, Dingcheng Yue +2
Increasing popularity of high-dynamic-range (HDR) image and video content brings the need for metrics that could predict the severity of image impairments as seen on displays of di…
Noise-Aware Merging of High Dynamic Range Image Stacks without Camera Calibration
Param Hanji, Fangcheng Zhong, Rafal K. Mantiuk
A near-optimal reconstruction of the radiance of a High Dynamic Range scene from an exposure stack can be obtained by modeling the camera noise distribution. The latent radiance is…