Most image fusion methods are designed for ideal scenarios and struggle to handle noise. Existing noise-aware fusion methods are supervised and heavily rely on constructed paired data, limiting performance and generalization. This paper proposes a novel unsupervised noisy visible and infrared image fusion method, comprising two key modules. First, when only noisy source images are available, a convolutional low-rank optimization module decomposes clean components based on convolutional low-rank priors, guiding subsequent optimization. The unsupervised approach eliminates data dependency and enhances generalization across various and variable noise. Second, a unified network jointly realizes denoising and fusion. It consists of both intra-modal recovery and inter-modal recovery and fusion, also with a convolutional low-rankness loss for regularization. By exploiting the commonalities of denoising and fusion, the joint framework significantly reduces network complexity while expanding functionality. Extensive experiments validate the effectiveness and generalization of the proposed method for image fusion under various and variable noise conditions. The code is publicly available at https://github.com/hanna-xu/Deno-IF.

Blank defect mitigation is crucial for extreme ultraviolet (EUV) lithography. One of the existing options is to relocate patterns to avoid defect impact. However, when the defect number increases, only pattern shift in X-Y directions becomes far from enough, requiring the reticle holder rotate a small angle to provide a third exploring dimension. This non-trivial extension from 2D to 3D exploration requests efficient runtime as well as enough accuracy to handle different defect sizes and locations on the different features. In this paper, we present the first work with a detailed algorithm to find the optimal shift and rotation for layout patterns on blanks. Compared to the straightforward method, which is to check every pair of defect and feature at every possible relocation position, our proposed algorithm can significantly reduce the runtime complexity to scale linearly with the size of the full solution space. The experimental results validate our method and show a largely increased success rate of defect mitigation by shift and rotation.