Sorry, you need to enable JavaScript to visit this website.

Sparse Modeling in Image Processing and Deep LearningSparse approximation is a well-established theory, with a profound impact on the fields of signal and image processing. In this talk we start by presenting this model and its features, and then turn to describe two special cases of it – the convolutional sparse coding (CSC) and its multi-layered version (ML-CSC).  Amazingly, as we will carefully show, ML-CSC provides a solid theoretical foundation to … deep-learning.

SUPPLEMENTARY_MATERIALS_FOR__EVENT_DENOISING_BASED_ON_ITERATIVE_TREE_STRUCTURED_INFORMATION_AGGREGATION

Supplementary material for the paper titled "TARGET DRIVEN ADAPTIVE LOSS FOR INFRARED SMALL TARGET DETECTION"

SUPPLEMENTARY MATERIALS FOR EVENT DENOISING BASED ON ITERATIVE
TREE-STRUCTURED INFORMATION AGGREGATIO

Recent advancements in diffusion models have enabled high-fidelity and photorealistic image generation across diverse applications. However, these models also present security and privacy risks, including copyright violations, sensitive information leakage, and the creation of harmful or offensive content that could be exploited maliciously. In this study, we uncover a novel security threat where an attacker leverages diffusion model APIs to generate synthetic images, which are then used to train a high-performing substitute model.

Dense image matching aims to find a correspondent for every pixel of a source image in a partially overlapping target image. State-of-the-art methods typically rely on a coarse-to-fine mechanism where a single correspondent hypothesis is produced per source location at each scale. In challenging cases -- such as at depth discontinuities or when the target image is a strong zoom-in of the source image -- the correspondents of neighboring source locations are often widely spread and predicting a single correspondent hypothesis per source location at each scale may lead to erroneous matches.

Sonar imaging is fundamental to underwater exploration, with critical applications in defense, navigation, and marine research. Shadow regions, in particular, provide essential cues for object detection and classification, yet existing studies primarily focus on highlight-based analysis, leaving shadow-based classification underexplored. To bridge this gap, we propose a Context-adaptive sonar image classification framework that leverages advanced image processing techniques to extract and integrate discriminative shadow and highlight features.

Despeckling of Synthetic Aperture Radar (SAR) images has seen significant progress in recent years, largely driven by advancements in deep learning techniques. However, many of these approaches face challenges when applied to new SAR datasets, primarily due to their dependence on ground truth images, which are often unavailable for real-world sensors. In this paper, we address this limitation by extending the concept of unbiased risk estimation in the presence of Gamma-distributed multiplicative speckle.

Supplementary Materials for Diffusion Pretraining for Gait Recognition in the Wild

Supplementary Material for EventEgoHands: Event-based Egocentric 3D Hand Mesh Reconstruction