In this work, we propose a model-driven deep learning method by unrolling the recently developed ReLU-based Hard Thresholding (RHT) algorithm for non-negative sparse signal recovery. Specifically, we ...

Diffusion probabilistic models (DPMs) have achieved impressive success in high-resolution image synthesis, especially in recent large-scale text-to-image generation applications. An essential ...

A research team at Shanghai Jiao Tong University has developed a systematic framework that combines semantic-segmentation AI ...

By combining image-based phenotyping with gene expression analysis, the research reveals significant variability in root morphology and ...

Moroccan scientists have developed a new method to automatically locate and label solar panels in large photovoltaic ...

Scientists in Morocco have developed a method that uses the metadata of PV plants’ infrared images to label them geographically. The automatic database can then be used in deep learning models and ...

Above, we demonstrated that thresholding produces a messy result for low-contrast images, so Weka is clearly preferred for such images. However, it is worth considering whether thresholding is a ...

Immunoreagents, most commonly antibodies, are integral components of lateral flow immunoassays. However, the use of antibodies comes with limitations, particularly relating to their reproducible ...

Keywords: wavelet transform, wavelet thresholding, image noise reduction, adaptive thresholding, MSE, PSNR, SSIM Citation: Pereira Neto A and Barros FJB (2025) Noise reduction in brain magnetic ...

The first month of life is a critical period for brain development, with rapid reconfiguration of brain structure and function. Yet, how structural and functional connections reorganize to support ...