Deep learning has revolutionized the way we tackle computer vision problems.

The increasing realism of AI-generated imagery poses challenges for verifying visual authenticity. We present an explainable image authenticity detection system that combines a lightweight convolutional classifier ("Faster-Than-Lies") with a Vision-Language Model (Qwen2-VL-7B) to classify, localize, and explain artifacts in 32x32 images. Our model achieves 96.5% accuracy on the extended CiFAKE dataset augmented with adversarial perturbations and maintains an inference time of 175ms on 8-core CPUs, enabling deployment on local or edge devices. Using autoencoder-based reconstruction error maps, we generate artifact localization heatmaps, which enhance interpretability for both humans and the VLM. We further categorize 70 visual artifact types into eight semantic groups and demonstrate explainable text generation for each detected anomaly. This work highlights the feasibility of combining visual and linguistic reasoning for interpretable authenticity detection in low-resolution imagery and outlines potential cross-domain applications in forensics, industrial inspection, and social media moderation.

High-quality observations of hub-height winds are valuable but sparse in space and time. Simulations are widely available on regular grids but are generally biased and too coarse to inform wind-farm siting or to assess extreme-weather-related risks (e.g., gusts) at infrastructure scales. To fully utilize both data types for generating high-quality, high-resolution hub-height wind speeds (tens to ~100m above ground), this study introduces WindSR, a diffusion model with data assimilation for super-resolution downscaling of hub-height winds. WindSR integrates sparse observational data with simulation fields during downscaling using state-of-the-art diffusion models. A dynamic-radius blending method is introduced to merge observations with simulations, providing conditioning for the diffusion process. Terrain information is incorporated during both training and inference to account for its role as a key driver of winds. Evaluated against convolutional-neural-network and generative-adversarial-network baselines, WindSR outperforms them in both downscaling efficiency and accuracy. Our data assimilation reduces WindSR's model bias by approximately 20% relative to independent observations.

Traditional video compression algorithms exhibit significant quality degradation at extremely low bitrates. Promptus emerges as a new paradigm for video streaming, substantially cutting down the bandwidth essential for video streaming. However, Promptus is computationally intensive and can not run in real-time on mobile devices. This paper presents PromptMobile, an efficient acceleration framework tailored for on-device Promptus. Specifically, we propose (1) a two-stage efficient generation framework to reduce computational cost by 8.1x, (2) a fine-grained inter-frame caching to reduce redundant computations by 16.6\%, (3) system-level optimizations to further enhance efficiency. The evaluations demonstrate that compared with the original Promptus, PromptMobile achieves a 13.6x increase in image generation speed. Compared with other streaming methods, PromptMobile achives an average LPIPS improvement of 0.016 (compared with H.265), reducing 60\% of severely distorted frames (compared to VQGAN).

Satellites are widely used to estimate and monitor ground cover, providing critical information to address the challenges posed by climate change. High-resolution satellite images help to identify smaller features on the ground and classification of ground cover types. Small satellites have become very popular recently due to their cost-effectiveness. However, smaller satellites have weaker spatial resolution, and preprocessing using recent generative models made it possible to enhance the resolution of these satellite images. The objective of this paper is to propose computationally efficient guided or image-conditioned denoising diffusion models (DDMs) to perform super-resolution on low-quality images. Denoising based on stochastic ordinary differential equations (ODEs) typically takes hundreds of iterations and it can be reduced using deterministic ODEs. I propose Consistency Models (CM) that utilize deterministic ODEs for efficient denoising and perform super resolution on satellite images. The DOTA v2.0 image dataset that is used to develop object detectors needed for urban planning and ground cover estimation, is used in this project. The Stable Diffusion model is used as the base model, and the DDM in Stable Diffusion is converted into a Consistency Model (CM) using Teacher-Student Distillation to apply deterministic denoising. Stable diffusion with modified CM has successfully improved the resolution of satellite images by a factor of 16, and the computational time was reduced by a factor of 20 compared to stochastic denoising methods. The FID score of low-resolution images improved from 10.0 to 1.9 after increasing the image resolution using my algorithm for consistency models.

Over a century ago, the microscopist Ernst Abbe devised an equation showing how the resolution of an optical microscope is limited by the wavelength of the illumination light [1]. This critical limitation, known as the Abbe's diffraction limit, implies that it is not possible to resolve objects smaller than 200 nanometers using an optical microscope. For scale, the diameter of a DNA molecule is about 2.5 nanometers--approximately one hundred times smaller. Since then, the quest to overcome this limit and to develop techniques for highresolution imaging of cellular and subcellular structures has led to significant advancements in biomedical research [2, 3, 4, 5, 6, 7] paving the way for super-resolution microscopy. The super-resolution techniques that have revolutionized the field include structured illumination microscopy (SIM) [6, 7], stimulated emission depletion (STED) [2, 3], stochastic optical reconstruction microscopy (STORM) [5], and photoactivated localization microscopy (PALM) [4]. However, these techniques require complex and expensive instrumentation, limiting their widespread availability.

In spite of great success in many image recognition tasks achieved by recent deep models, directly applying them to recognize low-resolution images may suffer from low accuracy due to the missing of informative details during resolution degradation. However, these images are still recognizable for subjects who are familiar with the corresponding high-resolution ones. Inspired by that, we propose a teacher-student learning approach to facilitate low-resolution image recognition via hybrid order relational knowledge distillation. The approach refers to three streams: the teacher stream is pretrained to recognize high-resolution images in high accuracy, the student stream is learned to identify low-resolution images by mimicking the teacher's behaviors, and the extra assistant stream is introduced as bridge to help knowledge transfer across the teacher to the student. To extract sufficient knowledge for reducing the loss in accuracy, the learning of student is supervised with multiple losses, which preserves the similarities in various order relational structures. In this way, the capability of recovering missing details of familiar low-resolution images can be effectively enhanced, leading to a better knowledge transfer. Extensive experiments on metric learning, low-resolution image classification and low-resolution face recognition tasks show the effectiveness of our approach, while taking reduced models.

Coffee leaf rust, a foliar disease caused by the fungus Hemileia vastatrix, poses a major threat to coffee production, especially in Central America. Climate change further aggravates this issue, as it shortens the latency period between initial infection and the emergence of visible symptoms in diseases like leaf rust. Shortened latency periods can lead to more severe plant epidemics and faster spread of diseases. There is, hence, an urgent need for effective disease management strategies. To address these challenges, we explore the potential of deep learning models for enhancing early disease detection. However, deep learning models require extensive processing power and large amounts of data for model training, resources that are typically scarce. To overcome these barriers, we propose a preprocessing technique that involves convolving training images with a high-pass filter to enhance lesion-leaf contrast, significantly improving model efficacy in resource-limited environments. This method and our model demonstrated a strong performance, achieving over 90% across all evaluation metrics--including precision, recall, F1-score, and the Dice coefficient. Our experiments show that this approach outperforms other methods, including two different image preprocessing techniques and using unaltered, full-color images.

Abstract: This chapter provides an overview of deep learning techniques for improving the spatial resolution of MRI, ranging from convolutional neural networks, generative adversarial networks, to more advanced models including transformers, diffusion models, and implicit neural representations. Our exploration extends beyond the methodologies to scrutinize the impact of super-resolved images on clinical and neuroscientific assessments. We also cover various practical topics such as network architectures, image evaluation metrics, network loss functions, and training data specifics--including downsampling methods for simulating lowresolution images and dataset selection. Finally, we discuss existing challenges and potential future directions regarding the feasibility and reliability of deep learning-based MRI superresolution, with the aim to facilitate its wider adoption to benefit various clinical and neuroscientific applications. Keywords: Single-image super-resolution, deep learning, convolutional neural network, generative adversarial network, transformer, diffusion model, implicit neural representation, loss function, transfer learning, uncertainty estimation. Introduction MRI with higher spatial resolution provides more detailed insights into the structure and function of living human bodies non-invasively, which is highly desirable for accurate clinical diagnosis and image analysis. The spatial resolution of MRI is characterized by in-plane and through-plane resolutions (Figure 1). On the other hand, the through-plane resolution, also referred to as slice thickness, is determined differently for 2D and 3D imaging. In 2D imaging, the slice thickness is defined by the full width at half maximum (FWHM) of the slice-selection radiofrequency (RF) pulse profile. In 3D imaging, the slice-selection direction is encoded by another phase encoding gradient. Consequently, the through-plane resolution is determined similarly to the in-plane resolution by the maximal extent of the k-space along slice-selection direction as in Eq. 1. The in-plane resolution is dictated by the k-space coverage, and a larger k-space coverage brings higher spatial resolution (a). The slice thickness is determined by the slice-selective RF pulse for 2D imaging, and by k-space extent along sliceselection direction for 3D imaging (b).

Predictions of global climate models typically operate on coarse spatial scales due to the large computational costs of climate simulations. This has led to a considerable interest in methods for statistical downscaling, a similar process to super-resolution in the computer vision context, to provide more local and regional climate information. In this work, we apply conditional normalizing flows to the task of climate variable downscaling. We showcase its successful performance on an ERA5 water content dataset for different upsampling factors. Additionally, we show that the method allows us to assess the predictive uncertainty in terms of standard deviation from the fitted conditional distribution mean.