7 accepted papers
In this paper we develop a simple scheme for a coupled matrix factorization problem arising in the topic of hyperspectral super-resolution (HSR). HSR considers the problem of recovering a super-resolution image from a multispectral image and a hyperspectral image, which have lower spectral and spati…
In federated learning, models are learned from users’ data that are held private in their edge devices, by aggregating them in the service provider’s “cloud” to obtain a global model. Such global model is of great commercial value in, e.g., improving the customers’ experience. In this paper we focus…
Hyperspectral unmixing (HU) is a problem of blindly identifying the underlying materials, in form of spectral signatures, in the captured hyperspectral image. HU has received tremendous interest in remote sensing, and fundamentally the problem can be regarded as solving a simplex-structured matrix f…
Simplex-structured matrix factorization (SSMF) is a problem of recovering a basis matrix and the corresponding coefficient vectors from data, where the coefficient vectors are constrained to lie in the unit simplex. SSMF has attracted growing attention in recent years, with numerous applications suc…
Hyperspectral super-resolution (HSR) is a problem of recovering a high-spectral-spatial-resolution image from a multispectral measurement and a hyperspectral measurement, which have low spectral and spatial resolutions, respectively. We consider a low-rank structured matrix factorization formulation…
Consider a structured matrix factorizaton (SMF) whose coefficient vectors are constrained to lie in the unit simplex. This kind of simplex SMF (SSMF) has received growing attention and has found many applications such as hyperspectral unmixing in remote sensing, text mining in machine learning, and…
We propose a depth image denoising and enhancement framework using a light convolutional network. The network contains three layers for high dimension projection, missing data completion and image reconstruction. We jointly use both depth and visual images as inputs. For the gray image, we design a…