39 citations · 86 across the 14 of their papers we have counts for
20 papers
NILUT: Conditional Neural Implicit 3D Lookup Tables for Image Enhancement
Marcos V. Conde, Javier Vazquez-Corral, Michael S. Brown +1
3D lookup tables (3D LUTs) are a key component for image enhancement. Modern image signal processors (ISPs) have dedicated support for these as part of the camera rendering pipelin…
GamutMLP: A Lightweight MLP for Color Loss Recovery
Hoang M. Le, Brian Price, Scott Cohen +1
Cameras and image-editing software often process images in the wide-gamut ProPhoto color space, encompassing 90% of all visible colors. However, when images are encoded for sharing…
Day-to-Night Image Synthesis for Training Nighttime Neural ISPs
Abhijith Punnappurath, Abdullah Abuolaim, Abdelrahman Abdelhamed +2
Many flagship smartphone cameras now use a dedicated neural image signal processor (ISP) to render noisy raw sensor images to the final processed output. Training nightmode ISP net…
Learning sRGB-to-Raw-RGB De-rendering with Content-Aware Metadata
Seonghyeon Nam, Abhijith Punnappurath, Marcus A. Brubaker +1
Most camera images are rendered and saved in the standard RGB (sRGB) format by the camera's hardware. Due to the in-camera photo-finishing routines, nonlinear sRGB images are undes…
Noise2NoiseFlow: Realistic Camera Noise Modeling without Clean Images
Ali Maleky, Shayan Kousha, Michael S. Brown +1
Image noise modeling is a long-standing problem with many applications in computer vision. Early attempts that propose simple models, such as signal-independent additive white Gaus…
Modeling sRGB Camera Noise with Normalizing Flows
Shayan Kousha, Ali Maleky, Michael S. Brown +1
Noise modeling and reduction are fundamental tasks in low-level computer vision. They are particularly important for smartphone cameras relying on small sensors that exhibit visual…
Improving Single-Image Defocus Deblurring: How Dual-Pixel Images Help Through Multi-Task Learning
Abdullah Abuolaim, Mahmoud Afifi, Michael S. Brown
Many camera sensors use a dual-pixel (DP) design that operates as a rudimentary light field providing two sub-aperture views of a scene in a single capture. The DP sensor was devel…
Learning to Reduce Defocus Blur by Realistically Modeling Dual-Pixel Data
Abdullah Abuolaim, Mauricio Delbracio, Damien Kelly +2
Recent work has shown impressive results on data-driven defocus deblurring using the two-image views available on modern dual-pixel (DP) sensors. One significant challenge in this…
HistoGAN: Controlling Colors of GAN-Generated and Real Images via Color Histograms
Mahmoud Afifi, Marcus A. Brubaker, Michael S. Brown
While generative adversarial networks (GANs) can successfully produce high-quality images, they can be challenging to control. Simplifying GAN-based image generation is critical fo…
Interactive White Balancing for Camera-Rendered Images
Mahmoud Afifi, Michael S. Brown
White balance (WB) is one of the first photo-finishing steps used to render a captured image to its final output. WB is applied to remove the color cast caused by the scene's illum…
CIE XYZ Net: Unprocessing Images for Low-Level Computer Vision Tasks
Mahmoud Afifi, Abdelrahman Abdelhamed, Abdullah Abuolaim +2
Cameras currently allow access to two image states: (i) a minimally processed linear raw-RGB image state (i.e., raw sensor data) or (ii) a highly-processed nonlinear image state (e…
NTIRE 2020 Challenge on Real Image Denoising: Dataset, Methods and Results
Abdelrahman Abdelhamed, Mahmoud Afifi, Radu Timofte +87
This paper reviews the NTIRE 2020 challenge on real image denoising with focus on the newly introduced dataset, the proposed methods and their results. The challenge is a new versi…
A Little Bit More: Bitplane-Wise Bit-Depth Recovery
Abhijith Punnappurath, Michael S. Brown
Imaging sensors digitize incoming scene light at a dynamic range of 10--12 bits (i.e., 1024--4096 tonal values). The sensor image is then processed onboard the camera and finally q…
Defocus Deblurring Using Dual-Pixel Data
Abdullah Abuolaim, Michael S. Brown
Defocus blur arises in images that are captured with a shallow depth of field due to the use of a wide aperture. Correcting defocus blur is challenging because the blur is spatiall…
Deep White-Balance Editing
Mahmoud Afifi, Michael S. Brown
We introduce a deep learning approach to realistically edit an sRGB image's white balance. Cameras capture sensor images that are rendered by their integrated signal processor (ISP…
Learning Multi-Scale Photo Exposure Correction
Mahmoud Afifi, Konstantinos G. Derpanis, Björn Ommer +1
Capturing photographs with wrong exposures remains a major source of errors in camera-based imaging. Exposure problems are categorized as either: (i) overexposed, where the camera…
Sensor-Independent Illumination Estimation for DNN Models
Mahmoud Afifi, Michael S. Brown
While modern deep neural networks (DNNs) achieve state-of-the-art results for illuminant estimation, it is currently necessary to train a separate DNN for each type of camera senso…
Noise Flow: Noise Modeling with Conditional Normalizing Flows
Abdelrahman Abdelhamed, Marcus A. Brubaker, Michael S. Brown
Modeling and synthesizing image noise is an important aspect in many computer vision applications. The long-standing additive white Gaussian and heteroscedastic (signal-dependent)…
Classification Driven Dynamic Image Enhancement
Vivek Sharma, Ali Diba, Davy Neven +3
Convolutional neural networks rely on image texture and structure to serve as discriminative features to classify the image content. Image enhancement techniques can be used as pre…
Haze Visibility Enhancement: A Survey and Quantitative Benchmarking
Yu Li, Shaodi You, Michael S. Brown +1
This paper provides a comprehensive survey of methods dealing with visibility enhancement of images taken in hazy or foggy scenes. The survey begins with discussing the optical mod…