This paper reviews the NTIRE 2024 low light image enhancement challenge highlighting the
proposed solutions and results. The aim of this challenge is to discover an effective network …

This paper reviews the third biennial challenge on spectral reconstruction from RGB images,
ie, the recovery of whole-scene hyperspectral (HS) information from a 3-channel RGB …

Existing leading methods for spectral reconstruction (SR) focus on designing deeper or
wider convolutional neural networks (CNNs) to learn the end-to-end map** from the RGB …

Denoising is a crucial step for hyperspectral image (HSI) applications. Though witnessing
the great power of deep learning, existing HSI denoising methods suffer from limitations in …

In coded aperture snapshot spectral compressive imaging (CASSI) systems, hyperspectral
image (HSI) reconstruction methods are employed to recover the spatial-spectral signal from …

Well-known compressed sensing (CS) is widely used in image acquisition and
reconstruction. However, accurately reconstructing images from measurements at low …

Coded aperture snapshot spectral imaging (CASSI) is an important technique for capturing
three-dimensional (3D) hyperspectral images (HSIs), and involves an inverse problem of …

Snapshot compressive imaging (SCI) systems aim to capture high-dimensional (≥ 3 D)
images in a single shot using 2D detectors. SCI devices consist of two main parts: a …

Edge detection is represented as one of the most challenging tasks in computer vision, due
to the complexity of detecting the edges or boundaries in real-world images that contains …

Transformers have become the state-of-the-art architectures for various tasks in Natural
Language Processing (NLP) and Computer Vision (CV); however, their space and …