6 accepted papers
We present QuCNet, a hybrid quantum classical network for efficient remote sensing image classification. QuCNet integrates a lightweight convolutional encoder with sixteen parallel four-qubit trainable quantum circuits (TQCs) trained under a Hybrid Cyclic Weight-Sharing (HCWS) strategy. This design
Burst image processing is becoming increasingly popular in recent years. However, it is a challenging task since individual burst images undergo multiple degradations and often have mutual misalignments resulting in ghosting and zipper artifacts. Existing burst restoration methods usually do not con…
Video motion magnification makes subtle invisible motions visible, such as small chest movements while breathing, subtle vibrations in the moving objects etc. But small motions are prone to noise, illumination changes, large motions, etc. making the task difficult. Most state-of-the-art methods use…
Weather degraded conditions such as rain, haze, snow, etc. may degrade the performance of most computer vision systems. Therefore, effective restoration of multi-weather degraded images is an essential prerequisite for successful functioning of such systems. The current multi-weather image restorati…
Moving object segmentation in videos (MOS) is a highly demanding task for security-based applications like automated outdoor video surveillance. Most of the existing techniques proposed for MOS are highly depend on fine-tuning a model on the first frame(s) of test sequence or complicated training pr…
The quality of images captured in bad weather is often affected by chromatic casts and low visibility due to the presence of atmospheric particles. Restoration of the color balance is often ignored in most of the existing image de-hazing methods. In this paper, we propose a varicolored end-to-end im…