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Found 15 papers, 2 papers with code
Joint Sampling and Optimisation for Inverse Rendering
By combining proportional and finite-difference samples, we continuously reduce the variance of our novel gradient meta-estimators throughout the optimisation process.
Enhancing image quality prediction with self-supervised visual masking
Full-reference image quality metrics (FR-IQMs) aim to measure the visual differences between a pair of reference and distorted images, with the goal of accurately predicting human judgments.
Revisiting Image Deblurring with an Efficient ConvNet
In this work, we propose a unified lightweight CNN network that features a large effective receptive field (ERF) and demonstrates comparable or even better performance than Transformers while bearing less computational costs.
Ranked #2 on
Image Defocus Deblurring
on DPD
GlowGAN: Unsupervised Learning of HDR Images from LDR Images in the Wild
Most in-the-wild images are stored in Low Dynamic Range (LDR) form, serving as a partial observation of the High Dynamic Range (HDR) visual world.
Video frame interpolation for high dynamic range sequences captured with dual-exposure sensors
Video frame interpolation (VFI) enables many important applications that might involve the temporal domain, such as slow motion playback, or the spatial domain, such as stop motion sequences.
Eikonal Fields for Refractive Novel-View Synthesis
We tackle the problem of generating novel-view images from collections of 2D images showing refractive and reflective objects.
Learning a self-supervised tone mapping operator via feature contrast masking loss
High Dynamic Range (HDR) content is becoming ubiquitous due to the rapid development of capture technologies.
Learning GAN-based Foveated Reconstruction to Recover Perceptually Important Image Features
Given the nature of GAN-based solutions, we focus on the sensitivity of human vision to hallucination in case of input samples with different densities.
HDR Denoising and Deblurring by Learning Spatio-temporal Distortion Models
Regrettably, capturing DISTORTED sensor readings is time-consuming; as well, there is a lack of CLEAN HDR videos.
Perceptual error optimization for Monte Carlo rendering
To find solutions, we present a set of algorithms that provide varying trade-offs between quality and speed, showing substantial improvements over prior state of the art.
Image Generation
Graphics
I.3.7
X-Fields: Implicit Neural View-, Light- and Time-Image Interpolation
We suggest to represent an X-Field -a set of 2D images taken across different view, time or illumination conditions, i. e., video, light field, reflectance fields or combinations thereof-by learning a neural network (NN) to map their view, time or light coordinates to 2D images.
Neural View-Interpolation for Sparse Light Field Video
We suggest representing light field (LF) videos as "one-off" neural networks (NN), i. e., a learned mapping from view-plus-time coordinates to high-resolution color values, trained on sparse views.
Towards a quality metric for dense light fields
We find that the existing image quality metrics provide good measures of light-field quality, but require dense reference light- fields for optimal performance.
Video Quality Assessment for Computer Graphics Applications
We present a full-reference video quality metric geared specifically towards the requirements of Computer Graphics applications as a faster computational alternative to subjective evaluation.
Dynamic Range Independent Image Quality Assessment
Current quality assessment metrics are not suitable for this task, as they assume that both reference and test images have the same dynamic range.
