Low-light photography presents significant challenges. Multi-image processing methods
have made numerous attempts to obtain high-quality photos yet remain unsatisfactory …

Deep learning techniques have received much attention in the area of image denoising.
However, there are substantial differences in the various types of deep learning methods …

Abstract Neural Radiance Fields (NeRF) is a technique for high quality novel view synthesis
from a collection of posed input images. Like most view synthesis methods, NeRF uses …

The recent explosive interest on transformers has suggested their potential to become
powerful``universal" models for computer vision tasks, such as classification, detection, and …

Learning a single static convolutional kernel in each convolutional layer is the common
training paradigm of modern Convolutional Neural Networks (CNNs). Instead, recent …

Recent advances in deep learning have been pushing image denoising techniques to a
new level. In self-supervised image denoising, blind-spot network (BSN) is one of the most …

We propose an end-to-end trainable Convolutional Neural Network (CNN), named
GridDehazeNet, for single image dehazing. The GridDehazeNet consists of three modules …

This paper reviews the NTIRE 2024 RAW Image Super-Resolution Challenge highlighting
the proposed solutions and results. New methods for RAW Super-Resolution could be …

While deep convolutional neural networks (CNNs) have achieved impressive success in
image denoising with additive white Gaussian noise (AWGN), their performance remains …

Feature upsampling is a key operation in a number of modern convolutional network
architectures, eg feature pyramids. Its design is critical for dense prediction tasks such as …