# Adaptive Image Sampling using Deep Learning and its Application on X-Ray Fluorescence Image Reconstruction

arXiv: https://arxiv.org/abs/1812.10836 · HTML: https://mlsanity.com/paper/1812.10836/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Dec 2018.

## Built on by (most cited first, 9 of 9)

- [Deep Learning-based Image and Video Inpainting: A Survey](https://mlsanity.com/paper/2401.03395/) (2024)
- [Learning Sparse Masks for Diffusion-based Image Inpainting](https://mlsanity.com/paper/2110.02636/) (2021)
- [Adaptive Illumination based Depth Sensing using Deep Superpixel and Soft Sampling Approximation](https://mlsanity.com/paper/2103.12297/) (2021)
- [Efficient Neural Generation of 4K Masks for Homogeneous Diffusion Inpainting](https://mlsanity.com/paper/2303.10096/) (2023)
- [Prior based Sampling for Adaptive LiDAR](https://mlsanity.com/paper/2304.07099/) (2023)
- [ganX -- generate artificially new XRF a python library to generate MA-XRF raw data out of RGB images](https://mlsanity.com/paper/2304.14078/) (2023)
- [Exploring Kernel Transformations for Implicit Neural Representations](https://mlsanity.com/paper/2504.04728/) (2025)
- [A Wasserstein GAN for Joint Learning of Inpainting and Spatial Optimisation](https://mlsanity.com/paper/2202.05623/) (2022)
- [Denoising Fast X-Ray Fluorescence Raster Scans of Paintings](https://mlsanity.com/paper/2206.01740/) (2022)

## Abstract (arXiv)

This paper presents an adaptive image sampling algorithm based on Deep Learning (DL). The adaptive sampling mask generation network is jointly trained with an image inpainting network. The sampling rate is controlled in the mask generation network, and a binarization strategy is investigated to make the sampling mask binary. Besides the image sampling and reconstruction application, we show that the proposed adaptive sampling algorithm is able to speed up raster scan processes such as the X-Ray fluorescence (XRF) image scanning process. Recently XRF laboratory-based systems have evolved to lightweight and portable instruments thanks to technological advancements in both X-Ray generation and detection. However, the scanning time of an XRF image is usually long due to the long exposures requires (e.g., 100 μ s-1ms per point). We propose an XRF image inpainting approach to address the issue of long scanning time, thus speeding up the scanning process while still maintaining the possibility to reconstruct a high quality XRF image. The proposed adaptive image sampling algorithm is applied to the RGB image of the scanning target to generate the sampling mask. The XRF scanner is then driven according to the sampling mask to scan a subset of the total image pixels. Finally, we inpaint the scanned XRF image by fusing the RGB image to reconstruct the full scan XRF image. The experiments show that the proposed adaptive sampling algorithm is able to effectively sample the image and achieve a better reconstruction accuracy than that of the existing methods.