Image Processing

Doppler time-of-flight (Do-ToF) imaging has recently attracted significant attention due to its high-resolution capabilities for measuring radial velocity. However, a challenge arises when the back-reflected signal received by pixels switches between a moving object and a stationary background, leading to the appearance of edge artifacts in the velocity images. To address this issue, we propose a per-pixel gradient-based method for identifying and correcting these artifacts.

To evaluate SARNet’s generalization, we captured a real-world stereo dataset in Guangzhou using a binocular camera. The dataset includes diverse urban and natural scenes to assess SARNet’s performance beyond synthetic and benchmark datasets. Fig. 7 illustrates SARNet’s predictions on real-world scenes, KITTI 2012, and KITTI 2015. Experimental results demonstrate that SARNet generates clear and consistent disparity maps across both smooth and complex regions, highlighting its robustness in real-world depth estimation tasks.

The Ancient Handwritten Devanagari Documents dataset is a curated collection of historical manuscripts written in the Devanagari script. It comprises digitized images of handwritten texts from various periods, containing diverse calligraphic styles, degradations, and linguistic variations. This dataset is designed for research in optical character recognition (OCR), handwritten text recognition (HTR), word spotting in historical documents and linguistic analysis.

This dataset comprises 33,800 images of underwater signals captured in aquatic environments. Each signal is presented against three types of backgrounds: pool, marine, and plain white. Additionally, the dataset includes three water tones: clear, blue, and green. A total of 12 different signals are included, each available in all six possible background-tone combinations.