A de-interlacing algorithm using adaptive 4-field motion compensation approach is presented. It consists of blockbased directional edge interpolation, same-parity 4-field motion detection, 4-field motion estimation and compensation. The intra field methods are reconstructed the frame from the current field information .but this method introduce the edge flicker problems and jitter effect. The inter field methods are depends on the previous and future fields for reconstruction of the current frame. This method introduces feathering effect. The edges are sharper when the directional edge interpolation is adopted and jitter effect and the feathering effect eliminated. The motion adaptive deinterlacing scheme is taking the advantages of both intra and inters field methods. First it finds the motion by using motion detection scheme if the field contain motion apply intra field interpolation method if the field contain stationary objects apply the inter field interpolation method. The 3-field motion detection can not detect the fast motion areas from field to field. The same parity 4-field motion adaptive deinterlacing and the 4-field motion compensation detect the static areas and fast motion by four reference fields. The Compensation recovers the interlaced videos to the progressive ones but the feathering effect is not recovered in this method. The adaptive 4-field motion compensation method removes the feathering effect along with detecting fast motion areas by using four reference fields. Experimental results show that the peak signal-to-noise ratio of our adaptive 4-field motion compensation deinterlacing algorithm is 4 to 6 dB higher than that of 3-field motion adaptive deinterlacing and 2 to 3 dB higher than 4-field motion compensation deinterlacing and attain the best quality of video.
This paper aims to develop a novel deep learning concept to deal with video inpainting. Initially, motion tracking is performed, which is the process of determining motion vectors that describe the transformation from adjacent frames in a video sequence. Further, the regions or patches of each frame are categorized using the optimized recurrent neural network (RNN), in which the region is split into a smooth and structure region. It is performed using the texture feature called grey-level co-occurrence matrix. The filling of the smooth region is accomplished by replacing with the mean pixels of unmasked region, and the structure region is done by optimized patch matching approach based on scale-invariant feature transform (SIFT). The main objective optimized patch matching is based on the minimized Euclidean distance between the extracted SIFT features of the original patch and reference patch, and the certain patch is inpainted by the optimized patch. Here, the hybridization of two meta-heuristic algorithms like cuckoo search algorithm and multi-verse optimization (MVO) called Cuckoo Search-based MVO is used to optimize the RNN and patch matching. Finally, the experimental results verify the reliability of the proposed algorithm over existing algorithms.
Edge detection is an essential processused to determine the object margins in most of the computer vision applications. Sobel edge detection algorithm, which is a simple method of edge detection, detects edges of various objects in an image. Real-time image applications need to be processed with large pixel data for a given time interval. So, Most of the VLSI architectures proposed for implementing sobeledge detection systems use FPGA, due to the parallel computing and reconfigurable feature. So, this paper introduces various VLSI architectures of sobel edge detection and comparesthe parameters like execution time, power dissipation with respect to similar input image size, different clock frequencies.
Video inpainting is the most trending research topic from the last decade. Video inpainting is the process of restoring the damaged parts of the vintage video or the filling of the regions by removing the unwanted objects with sophisticated techniques. The video inpainting is achieved by dividing the video into frames and the motion of the moving objects in the frames are tracked by applying the motion tracking method. The existing inpainting method proposed by the Criminisi, neglected the local similarities in the images so it suffered from dropping effect in the priority computation. This paper proposed a new priority computation method by introducing gradient operation with the addition of curvature in the data term and local structure measurement function with structure tensor theory as an additional term. Later, the patch matching is achieved with the Sum of Absolute Difference (SAD) distance method. Further, the optimal patch is selected by applying the Grey Wolf Optimization (GWO) algorithm. The efficiency of the proposed video inpainting technique is evaluated with the performance metrics, viz., Peak Signal to Noise Ratio (PSNR), Structural Similarity (SSIM), and Edge Similarity (ESIM) executed in MATLAB. The PSNR and SSIM of the proposed method for Fontaine_chatelet video is improved by 18.9% and 4.19% than existing method. The proposed method is compared with other existing methods also and it outperformed the existing methods.
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