Low-rank matrices play a fundamental role in modeling and computational methods for
signal processing and machine learning. In many applications where low-rank matrices …

Abstract Neural Radiance Field (NeRF) has gained considerable attention recently for 3D
scene reconstruction and novel view synthesis due to its remarkable synthesis quality …

Image representation is an important topic in computer vision and pattern recognition. It
plays a fundamental role in a range of applications toward understanding visual contents …

Abstract Model-based optimization methods and discriminative learning methods have been
the two dominant strategies for solving various inverse problems in low-level vision …

Regularization methods are a key tool in the solution of inverse problems. They are used to
introduce prior knowledge and allow a robust approximation of ill-posed (pseudo-) inverses …

Hyperspectral remote sensing images (HSIs) usually have high spectral resolution and low
spatial resolution. Conversely, multispectral images (MSIs) usually have low spectral and …

In-orbit optical-imaging instruments may suffer from degradations due to space environment
impacts or long-time operation. The degradation causes blurring on the image received from …

We present ExBluRF, a novel view synthesis method for extreme motion blurred images
based on efficient radiance fields optimization. Our approach consists of two main …

We study the question of reconstructing two signals f and g from their convolution y= f⁎ g.
This problem, known as blind deconvolution, pervades many areas of science and …

The Fourier domain is used in computer vision and machine learning as image analysis
tasks in the Fourier domain are analogous to spatial domain methods but are achieved …