KEYWORDS: Image restoration, Diffusion, Radio over Fiber, Performance modeling, Image processing, Anisotropic diffusion, Denoising, Visual process modeling, Medical imaging, Palladium
It is a difficult issue in image restoration to eliminate noise while avoiding the staircase effect and preserving edges. The anisotropic diffusion model proposed by Perona and Malik (PM) and the total variation model presented by Rudin, Osher, and Fatemi (ROF) are widely used to restore an image. However, the well-known defect of the two classic models is that they tend to cause the staircase effect. We propose a well-balanced anisotropic diffusion (WBAD) model by considering an adaptive balance parameter. The balance can be made in a selective way, meaning that it will alternate between the PM diffusion and ROF diffusion in accordance with the image features. The proposed WBAD model can preserve edges well while reducing noise, but it also causes less staircasing effect in the less smooth regions because it acts like the PM diffusion in these regions. Considering that the fourth-order PDEs can reduce the staircasing effect, we introduce a hybrid image restoration model based on an adaptive weight parameter to take advantage of the WBAD model and the fourth-order model. The experimental results illustrate that our algorithm can effectively remove noise and preserve edges. The higher values of peak signal-to-noise ratio and MSSIM highlight the better performance of our hybrid image restoration model.
Currently, the high complexity of video contents has posed the following major challenges for fast retrieval: (1) efficient similarity measurements, and (2) efficient indexing on the compact representations. A video-retrieval strategy based on fuzzy c-means (FCM) is presented for querying by example. Initially, the query video is segmented and represented by a set of shots, each shot can be represented by a key frame, and then we used video processing techniques to find visual cues to represent the key frame. Next, because the FCM algorithm is sensitive to the initializations, here we initialized the cluster center by the shots of query video so that users could achieve appropriate convergence. After an FCM cluster was initialized by the query video, each shot of query video was considered a benchmark point in the aforesaid cluster, and each shot in the database possessed a class label. The similarity between the shots in the database with the same class label and benchmark point can be transformed into the distance between them. Finally, the similarity between the query video and the video in database was transformed into the number of similar shots. Our experimental results demonstrated the performance of this proposed approach.
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