GSNet: a multi-class 3D attention-based hybrid glioma segmentation network.

In modern neuro-oncology, computer-aided biomedical image retrieval (CBIR) tools have recently gained significant popularity due to their quick and easy usage and high-performance capability. However, designing such an automated tool remains challenging because of the lack of balanced resources and inconsistent spatial texture. Like in many other fields of diagnosis, brain tumor (glioma) extraction has posed a challenge to the research community.

Correction of geometrically distorted underwater images using shift map analysis.

In underwater imaging, water waves cause severe geometric distortions and blurring of the acquired short-exposure images. Corrections for these distortions have been tackled reasonably well by previous efforts but still need improvement in the estimation of pixel shift maps to increase restoration accuracy. This paper presents a new algorithm that efficiently estimates the shift maps from geometrically distorted video sequences and uses those maps to restore the sequences.

Intelligent estimation of noise and blur variances using ANN for the restoration of ultrasound images.

Ultrasound (US) imaging is a widely used clinical diagnostic tool in medical imaging techniques. It is a comparatively safe, economical, painless, portable, and noninvasive real-time tool compared to the other imaging modalities. However, the image quality of US imaging is severely affected by the presence of speckle noise and blur during the acquisition process.

Geometric correction of atmospheric turbulence-degraded video containing moving objects.

Long-distance surveillance is a challenging task because of atmospheric turbulence that causes time-varying image shifts and blurs in images. These distortions become more significant as the imaging distance increases. This paper presents a new method for compensating image shifting in a video sequence while keeping real moving objects in the video unharmed.

Model-free prediction of atmospheric warp based on artificial neural network.

This paper presents the application of artificial neural network for predicting the warping of images of remote objects or scenes ahead of time. The algorithm is based on estimating the pattern of warping of previously captured short-exposure frames through a generalized regression neural network (GRNN) and then predicting the warping of the upcoming frame. A high-accuracy optical flow technique is employed to estimate the dense motion fields of the captured frames, which are considered as training data for the GRNN.

Simple and efficient approach for restoration of non-uniformly warped images.

A high accuracy image dewarping method is proposed to restore images from non-uniformly warped video sequences degraded by atmospheric turbulence. This approach contains three major steps. First, a non-rigid image registration technique is employed to register all the frames in the sequence to a reference frame and estimate the motion fields.