在神经网络应用中，不足的培训样本是一个常见的问题。尽管数据增强方法至少需要最少数量的样本，但我们提出了一种基于新颖的，基于渲染的管道来合成带注释的数据集。我们的方法不会修改现有样本，而是合成全新样本。提出的基于渲染的管道能够在全自动过程中生成和注释合成和部分真实的图像和视频数据。此外，管道可以帮助获取真实数据。拟议的管道基于渲染过程。此过程生成综合数据。部分实现的数据使合成序列通过在采集过程中合并真实摄像机使综合序列更接近现实。在自动车牌识别的背景下，广泛的实验验证证明了拟议的数据生成管道的好处，尤其是对于具有有限的可用培训数据的机器学习方案。与仅在实际数据集中训练的OCR算法相比，该实验表明，角色错误率和错过率分别从73.74％和100％和14.11％和41.27％降低。这些改进是通过仅对合成数据训练算法来实现的。当另外合并真实数据时，错误率可以进一步降低。因此，角色错误率和遗漏率可以分别降低至11.90％和39.88％。在实验过程中使用的所有数据以及针对自动数据生成的拟议基于渲染的管道公开可用（URL将在出版时揭示）。

在本文中，我们提出了一个用于光学特征识别（OCR）的数据增强框架。所提出的框架能够合成新的视角和照明方案，从而有效地丰富任何可用的OCR数据集。它的模块化结构允许修改以符合单个用户需求。该框架使得可以舒适地扩展可用数据集的扩大因子。此外，所提出的方法不仅限于单帧OCR，但也可以应用于视频OCR。我们通过扩大普通BRNO移动OCR数据集的15％子集来证明框架的性能。我们提出的框架能够利用OCR应用程序的性能，尤其是对于小型数据集。应用提出的方法，在字符错误率（CER）方面提高了多达2.79个百分点，并在子集中获得了高达7.88个百分点。特别是可以改善对具有挑战性的文本线条的认识。该类别的CER可能会降低14.92个百分点，而该级别的CER可下降到18.19个百分点。此外，与原始的非仪式完整数据集相比，使用建议方法的15％子集进行训练时，我们能够达到较小的错误率。

法医车牌识别（FLPR）仍然是在法律环境（例如刑事调查）中的公开挑战，在刑事调查中，不可读取的车牌（LPS）需要从高度压缩和/或低分辨率录像（例如监视摄像机）中解密。在这项工作中，我们提出了一个侧面信息变压器体系结构，该结构嵌入了输入压缩级别的知识，以改善在强压缩下的识别。我们在低质量的现实世界数据集上显示了变压器对车牌识别（LPR）的有效性。我们还提供了一个合成数据集，其中包括强烈退化，难以辨认的LP图像并分析嵌入知识对其的影响。该网络的表现优于现有的FLPR方法和标准最先进的图像识别模型，同时需要更少的参数。对于最严重的降级图像，我们可以将识别提高多达8.9％。

Self-supervised image denoising techniques emerged as convenient methods that allow training denoising models without requiring ground-truth noise-free data. Existing methods usually optimize loss metrics that are calculated from multiple noisy realizations of similar images, e.g., from neighboring tomographic slices. However, those approaches fail to utilize the multiple contrasts that are routinely acquired in medical imaging modalities like MRI or dual-energy CT. In this work, we propose the new self-supervised training scheme Noise2Contrast that combines information from multiple measured image contrasts to train a denoising model. We stack denoising with domain-transfer operators to utilize the independent noise realizations of different image contrasts to derive a self-supervised loss. The trained denoising operator achieves convincing quantitative and qualitative results, outperforming state-of-the-art self-supervised methods by 4.7-11.0%/4.8-7.3% (PSNR/SSIM) on brain MRI data and by 43.6-50.5%/57.1-77.1% (PSNR/SSIM) on dual-energy CT X-ray microscopy data with respect to the noisy baseline. Our experiments on different real measured data sets indicate that Noise2Contrast training generalizes to other multi-contrast imaging modalities.

Incorporating computed tomography (CT) reconstruction operators into differentiable pipelines has proven beneficial in many applications. Such approaches usually focus on the projection data and keep the acquisition geometry fixed. However, precise knowledge of the acquisition geometry is essential for high quality reconstruction results. In this paper, the differentiable formulation of fan-beam CT reconstruction is extended to the acquisition geometry. This allows to propagate gradient information from a loss function on the reconstructed image into the geometry parameters. As a proof-of-concept experiment, this idea is applied to rigid motion compensation. The cost function is parameterized by a trained neural network which regresses an image quality metric from the motion affected reconstruction alone. Using the proposed method, we are the first to optimize such an autofocus-inspired algorithm based on analytical gradients. The algorithm achieves a reduction in MSE by 35.5 % and an improvement in SSIM by 12.6 % over the motion affected reconstruction. Next to motion compensation, we see further use cases of our differentiable method for scanner calibration or hybrid techniques employing deep models.

Our goal is to reconstruct tomographic images with few measurements and a low signal-to-noise ratio. In clinical imaging, this helps to improve patient comfort and reduce radiation exposure. As quantum computing advances, we propose to use an adiabatic quantum computer and associated hybrid methods to solve the reconstruction problem. Tomographic reconstruction is an ill-posed inverse problem. We test our reconstruction technique for image size, noise content, and underdetermination of the measured projection data. We then present the reconstructed binary and integer-valued images of up to 32 by 32 pixels. The demonstrated method competes with traditional reconstruction algorithms and is superior in terms of robustness to noise and reconstructions from few projections. We postulate that hybrid quantum computing will soon reach maturity for real applications in tomographic reconstruction. Finally, we point out the current limitations regarding the problem size and interpretability of the algorithm.

The existence of metallic implants in projection images for cone-beam computed tomography (CBCT) introduces undesired artifacts which degrade the quality of reconstructed images. In order to reduce metal artifacts, projection inpainting is an essential step in many metal artifact reduction algorithms. In this work, a hybrid network combining the shift window (Swin) vision transformer (ViT) and a convolutional neural network is proposed as a baseline network for the inpainting task. To incorporate metal information for the Swin ViT-based encoder, metal-conscious self-embedding and neighborhood-embedding methods are investigated. Both methods have improved the performance of the baseline network. Furthermore, by choosing appropriate window size, the model with neighborhood-embedding could achieve the lowest mean absolute error of 0.079 in metal regions and the highest peak signal-to-noise ratio of 42.346 in CBCT projections. At the end, the efficiency of metal-conscious embedding on both simulated and real cadaver CBCT data has been demonstrated, where the inpainting capability of the baseline network has been enhanced.

Computer-aided systems in histopathology are often challenged by various sources of domain shift that impact the performance of these algorithms considerably. We investigated the potential of using self-supervised pre-training to overcome scanner-induced domain shifts for the downstream task of tumor segmentation. For this, we present the Barlow Triplets to learn scanner-invariant representations from a multi-scanner dataset with local image correspondences. We show that self-supervised pre-training successfully aligned different scanner representations, which, interestingly only results in a limited benefit for our downstream task. We thereby provide insights into the influence of scanner characteristics for downstream applications and contribute to a better understanding of why established self-supervised methods have not yet shown the same success on histopathology data as they have for natural images.

The French National Institute of Geographical and Forest Information (IGN) has the mission to document and measure land-cover on French territory and provides referential geographical datasets, including high-resolution aerial images and topographic maps. The monitoring of land-cover plays a crucial role in land management and planning initiatives, which can have significant socio-economic and environmental impact. Together with remote sensing technologies, artificial intelligence (IA) promises to become a powerful tool in determining land-cover and its evolution. IGN is currently exploring the potential of IA in the production of high-resolution land cover maps. Notably, deep learning methods are employed to obtain a semantic segmentation of aerial images. However, territories as large as France imply heterogeneous contexts: variations in landscapes and image acquisition make it challenging to provide uniform, reliable and accurate results across all of France. The FLAIR-one dataset presented is part of the dataset currently used at IGN to establish the French national reference land cover map "Occupation du sol \`a grande \'echelle" (OCS- GE).

Tumor segmentation in histopathology images is often complicated by its composition of different histological subtypes and class imbalance. Oversampling subtypes with low prevalence features is not a satisfactory solution since it eventually leads to overfitting. We propose to create synthetic images with semantically-conditioned deep generative networks and to combine subtype-balanced synthetic images with the original dataset to achieve better segmentation performance. We show the suitability of Generative Adversarial Networks (GANs) and especially diffusion models to create realistic images based on subtype-conditioning for the use case of HER2-stained histopathology. Additionally, we show the capability of diffusion models to conditionally inpaint HER2 tumor areas with modified subtypes. Combining the original dataset with the same amount of diffusion-generated images increased the tumor Dice score from 0.833 to 0.854 and almost halved the variance between the HER2 subtype recalls. These results create the basis for more reliable automatic HER2 analysis with lower performance variance between individual HER2 subtypes.