Videos account for a significant portion of internet traffic, and the presence of noise, whether from compression algorithms, low light, sensor imperfections, deteriorates the video quality. Ambient noise can also significantly diminish the visual quality. Traditional CNN-based video denoising methods rely on convolutional filters with fixed sizes and receptive fields, excelling at capturing local patterns and short-range dependencies. However, CNNs often struggle to handle long-term dependencies or relationships that extend over larger spatial and temporal scales. These are vital for accurately removing noise while preserving essential video details, textures, and structures. To address this limitation, we propose a novel approach, using UNet architecture, which combines the strengths of convolutional neural networks (CNNs) and graph neural networks (GNNs) for local and global information and dependency preservation. In this approach, CNN is followed by transformer attention for sparse graph formation for CNN. The spatiotemporal patches act as nodes, and the similarity between them represent edges. By integrating CNNs for local feature extraction followed by transformer attention and GNN for video denoising first time, for long-term spatio-temporal relationships, improves the ability to accurately model noise, preserve fine details and subsequently denoise videos more accurately. The strong ablation studies prove the effectiveness of the different modules, patch sizes on four different noise types. The proposed method outperformed most of the SOTA video denoising algorithms in terms of both PSNR and SSIM, at moderate computational cost, apart from the Video Restoration Transformer(VRT).

Quality video services have already gained high technical and commercial importance. The published work so far in this domain proposed mathematically and computationally complex algorithms, followed by the recent training-greedy deep learning-based denoising algorithms. This work proposes a video-denoising algorithm based on multiple UNet networks. The proposed video-denoising algorithm uses multiple encoder-decoder networks for video noise residual frame estimation, un-like the single encoder-decoder used by the published denoising algorithms. Using multiple skip connection UNets, we increase the residual noise modeling accuracy while restricting the signal features, which helps to improve denoising performance. The proposed network is trained end-to-end without motion compensation to reduce its complexity. The proposed network outperforms all the video denoising algorithms in terms of SSIM metric while it yields comparable performance in terms of PSNR.

Noise type and strength estimation are important in many image processing applications like denoising, compression, video tracking, etc. There are many existing methods for estimation of the type of noise and its strength in digital images. These methods mostly rely on the transform or spatial domain information of images. We propose a hybrid Discrete Wavelet Transform (DWT) and edge information removal based algorithm to estimate the strength of Gaussian noise in digital images. The wavelet coefficients corresponding to spatial domain edges are excluded from noise estimate calculation using a Sobel edge detector. The accuracy of the proposed algorithm is further increased using polynomial regression. Parseval's theorem mathematically validates the proposed algorithm. The performance of the proposed algorithm is evaluated on a standard LIVE image dataset. Benchmarking results show that the proposed algorithm outperforms all other state of the art algorithms by a large margin over a wide range of noise.

Tracking of ball in sports videos is one of the most challenging tasks in computer vision and video processing domain. Recent ball tracking approaches fail to handle tracking of a small size and fast moving ball. Inaccurate 2D ball detection leads to further deterioration of 3D ball tracking results. This paper presents a soccer ball tracking by detection approach using a pre-trained Convolutional Neural Network (CNN). The proposed algorithm used CNN for identifying ball from background and other moving objects like players and referees. The 2D ball detection results are fine-tuned for identifying true ball positions. True ball positions, from cameras shooting the scene from different angle, are further mapped on ground plane. The actual ball movement is tracked in 3D from top-view. Experiments show that the proposed algorithm can tackle challenges like small ball size, shape changes, occlusion and tracking high-speed balls.

This paper proposes a family of color image segmentation algorithms using genetic approach and color similarity threshold in terns of Just noticeable difference. Instead of segmenting and then optimizing, the proposed technique directly uses GA for optimized segmentation of color images. Application of GA on larger size color images is computationally heavy so they are applied on 4D-color image histogram table. The performance of the proposed algorithms is benchmarked on BSD dataset with color histogram based segmentation and Fuzzy C-means Algorithm using Probabilistic Rand Index (PRI). The proposed algorithms yield better analytical and visual results.

Robots capable of switching between snake-like and bipedal motion have advantages of greater manoeuvrability. This paper introduces ReBiS (Reconfigurable Bipedal Snake) robot, a novel modular design mechanism which can quickly transform between various configurations without rearrangement of modules. This paper documents the design as well as the gaits implemented on ReBiS. Possible gaits are divided into three categories; snake gaits, transforming gaits and walking gaits. An example gait, belonging each of the three categories, is implemented and presented here. Experimental verification demonstrated that the reconfiguration of this robot is swift and without reshuffling of modules.