Abstract
AbstractImage inpainting states to restoring misplaced procedure or damaged regions in an image. Image inpainting is a procedure of returning corrupted and old images. The major intention of this research is to analyze and justify the effectiveness of the image inpainting technique. Accordingly, performance analysis of image inpainting model is performed through Biharmonic functions. Moreover, the analysis is done by varying domain size in the Biharmonic function based on the percentage of images. Here, the performance of Biharmonic functions is evaluated using Structural Similarity Index Measure (SSIM), peak signal to noise ratio (PSNR), universal image quality index (UQI), second-derivative-like measure of enhancement (SDME), multi-scale structural similarity (MS-SSIM), and Mean Square Error (MSE). Thus, from this analysis, it is shown that the Biharmonic functions obtained better performance for the image inpainting process.KeywordsImage inpaintingBiharmonic functionsNoise reductionSuper-resolutionUniversal image quality indexPeak signal to noise ratioMulti-scale structural similaritySecond-derivative-like measure of enhancement
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