The field of anomaly detection and image denoising is witnessing significant advancements, driven by the development of innovative self-supervised and diffusion-based methods. Researchers are exploring new approaches to address the challenges of real-world image denoising, anomaly localization, and defect detection, with a focus on improving performance and efficiency. Notably, the use of anomaly priors, foreground-aware diffusion, and accelerated sampling trajectories is enabling the creation of more effective and controllable anomaly synthesis methods. These advancements have the potential to improve industrial anomaly segmentation and defect detection, with applications in various fields. Noteworthy papers include:

• Blind-Spot Guided Diffusion for Self-supervised Real-World Denoising, which proposes a novel dual-branch diffusion framework for state-of-the-art denoising performance.
• A Single Image Is All You Need: Zero-Shot Anomaly Localization Without Training Data, which introduces a single-image anomaly localization method that leverages the inductive bias of convolutional neural networks.
• Advancing Metallic Surface Defect Detection via Anomaly-Guided Pretraining on a Large Industrial Dataset, which presents a novel paradigm for anomaly-guided self-supervised pretraining that enhances performance in metallic surface defect detection.
• FAST: Foreground-aware Diffusion with Accelerated Sampling Trajectory for Segmentation-oriented Anomaly Synthesis, which proposes a foreground-aware diffusion framework for efficient and high-quality anomaly synthesis.