相关论文: Edge-Aware Extended Star-Tetrix Transforms for CFA…

Using integral transforms to the end of lines detection in images with complex background, makes the detection a hard task needing additional processing to manage the detection. As an integral transform, the Scale Space Radon Transform…

Despite advances in the paradigm of pre-training then fine-tuning in low-level vision tasks, significant challenges persist particularly regarding the increased size of pre-trained models such as memory usage and training time. Another…

The selection of high-redshift galaxies often involves spectral energy distribution (SED) fitting to photometric data, an expectation for contamination levels, and measurement of sample completeness -- all vetted through comparison to…

In the medical field, managing high-dimensional massive medical imaging data and performing reliable medical analysis from it is a critical challenge, especially in resource-limited environments such as remote medical facilities and mobile…

This paper focuses on improved edge model based on Curvelet coefficients analysis. Curvelet transform is a powerful tool for multiresolution representation of object with anisotropic edge. Curvelet coefficients contributions have been…

In recent years, many research achievements are made in the medical image fusion field. Medical Image fusion means that several of various modality image information is comprehended together to form one image to express its information. The…

Change detection (CD) aims to detect change regions within an image pair captured at different times, playing a significant role in diverse real-world applications. Nevertheless, most of the existing works focus on designing advanced…

Low-light images suffer from complex degradation, and existing enhancement methods often encode all degradation factors within a single latent space. This leads to highly entangled features and strong black-box characteristics, making the…

The application of the context-adaptive entropy model significantly improves the rate-distortion (R-D) performance, in which hyperpriors and autoregressive models are jointly utilized to effectively capture the spatial redundancy of the…

After a pedagogical introduction to the main concepts of synthetic photometry, colours and bolometric corrections in the Johnson-Cousins, 2MASS, and HST-ACS/WFC3 photometric systems are generated from MARCS synthetic fluxes for various…

X-ray absorption fine structure (XAFS) and x-ray emission spectroscopy (XES) are advanced x-ray spectroscopies that impact a wide range of disciplines. However, unlike the majority of other spectroscopic methods, XAFS and XES are…

Transformers are very powerful tools for a variety of tasks across domains, from text generation to image captioning. However, transformers require substantial amounts of training data, which is often a challenge in biomedical settings,…

Context. The surface structures and dynamics of cool stars are characterized by the presence of convective motions and turbulent flows which shape the emergent spectrum. Aims. We used realistic three-dimensional radiative hydrodynamical…

Extended x-ray absorption fine-structure (EXAFS) data collected in the fluorescence mode are susceptible to an apparent amplitude reduction due to the self-absorption of the fluorescing photon by the sample before it reaches a detector.…

We present T-PHOT, a publicly available software aimed at extracting accurate photometry from low-resolution images of deep extragalactic fields, where the blending of sources can be a serious problem for the accurate and unbiased…

Downsampling images and labels, often necessitated by limited resources or to expedite network training, leads to the loss of small objects and thin boundaries. This undermines the segmentation network's capacity to interpret images…

Foundation models pre-trained on large-scale data have been widely witnessed to achieve success in various natural imaging downstream tasks. Parameter-efficient fine-tuning (PEFT) methods aim to adapt foundation models to new domains by…

Over the years, the Invariant Scattering Transform (IST) technique has become popular for medical image analysis, including using wavelet transform computation using Convolutional Neural Networks (CNN) to capture patterns' scale and…

For the last few decades, the application of signal-adaptive transform coding to video compression has been stymied by the large computational complexity of matrix-based solutions. In this paper, we propose a novel parametric approach to…

We propose X-NeRF, a novel method to learn a Cross-Spectral scene representation given images captured from cameras with different light spectrum sensitivity, based on the Neural Radiance Fields formulation. X-NeRF optimizes camera poses…