Hyperspectral image (HSI) restoration aims at recovering clean images from
degraded observations and plays a vital role in downstream tasks. Existing
model-based methods have limitations in accurately modeling the complex image
characteristics with handcraft priors, and deep learning-based methods suffer
from poor generalization ability. To alleviate these issues, this paper
proposes an unsupervised HSI restoration framework with pre-trained diffusion
model (HIR-Diff), which restores the clean HSIs from the product of two
low-rank components, i.e., the reduced image and the coefficient matrix.
Specifically, the reduced image, which has a low spectral dimension, lies in
the image field and can be inferred from our improved diffusion model where a
new guidance function with total variation (TV) prior is designed to ensure
that the reduced image can be well sampled. The coefficient matrix can be
effectively pre-estimated based on singular value decomposition (SVD) and
rank-revealing QR (RRQR) factorization. Furthermore, a novel exponential noise
schedule is proposed to accelerate the restoration process (about 5$\times$
acceleration for denoising) with little performance decrease. Extensive
experimental results validate the superiority of our method in both performance
and speed on a variety of HSI restoration tasks, including HSI denoising, noisy
HSI super-resolution, and noisy HSI inpainting. The code is available at
this https URL

本文提出了一种基于预训练扩散模型的无监督高光谱图像恢复框架（HIR-Diff），通过两个低秩分量（即降维图像和系数矩阵）的乘积来还原清晰的高光谱图像。实验证明，该方法在高光谱图像恢复任务中具有卓越的性能和速度优势。

HIR-Diff: 无监督的高光谱图像修复：改进的扩散模型

HIR-Diff: Unsupervised Hyperspectral Image Restoration Via Improved  Diffusion Models

Conventional approaches to image de-fencing have limited themselves to using
only image data in adjacent frames of the captured video of an approximately
static scene. In this work, we present a method to harness disparity using a
stereo pair of fenced images in order to detect fence pixels. Tourists and
amateur photographers commonly carry smartphones/phablets which can be used to
capture a short video sequence of the fenced scene. We model the formation of
the occluded frames in the captured video. Furthermore, we propose an
optimization framework to estimate the de-fenced image using the total
variation prior to regularize the ill-posed problem.

本文提出了一种使用视差及立体图像检测围栏像素、模拟拍摄视频中被挡住的图像帧，以及采用总变差先验正则化的优化方法来进行解围栏图像的方法。