Survey on Image Inpainting Methods

Survey on Image Inpainting MethodsMulukutla N V L KashyapID:1366945

ContentsWhat is Image Inpainting?Methods Under ReviewMethods OverviewExperimental ResultsConclusion

What is Image Inpainting?Image Inpainting refers to the task of filling in the missing or damaged regions of an image in an undetectable manner.Reconstruction of information in the missing or damaged regions inside an image so that the resulting image looks natural.Goal: To fill the missing region with perceptually plausible content using the information from rest of the image.Applications:Image CodingLost block recoveryScratch RemovalRemoval of objectsImage / Artwork restorationMethods Under ReviewThree methods:Structure and Texture InpaintingDecompose the image.Structure Inpainting and Texture Inpainting are performed.The results are combined.Multi-Resolution Image InpaintingConstruct Image Pyramid.Inpaint low resolution image.Consequently inpaint higher resolution images.Visual Resolution InpaintingTexture & Color Analysis

Structure & Texture InpaintingImage is decomposed into two functions:f = u + v .u: Image capturing the Basic Structure Componentv: Image capturing the Texture and Random Noise ComponentUse Structure Inpainting Algorithms on f1 to obtain reconstructed Image.Use Texture Synthesis Algorithms on f2.Merge both the Structure Inpainted & Texture Synthesized images.Experimental Results Structure & Texture Inpainting

Multi-Resolution InpaintingBuild an Image Pyramid.This is done by the method of down-sampling.Choose a low resolution image and inpaint it.Propagate this information to inpaint the higher resolution image just above it in the pyramid.We use a 3 x 3 patch on for inpainting the higher resolution image.We compute the Sum of Squared Difference values for filling the unknown pixel values.

Experimental Results Multi-Resolution Image Inpainting

Missing Block Completion

Specified Object Removal

Scratch Removal

Visual InpaintingTwo different perspectives:Texture AnalysisColor AnalysisUse n-level wavelet transform to separate the texturing image into different frequency components.We thus obtain multi-layer of the different frequency composition of the texture image.The repairing method must decide the initial level of the repairing pixel.

Experimental Results Image Inpainting based on human vision

ConclusionStructure & Texture InpaintingMulti- Resolution InpaintingVisual Resolution InpaintingThis method enables us to simultaneously recover texture and geometric information.

The key point is to select a novel combination of Structure and Texture Inpainting Algorithms.

Experimental results also proved high quality of inpainted images.Lower resolution image in the image pyramid can be inpainted with faster speed.

Advantages of this method include:better perceptual qualityfaster speed Better conformation to the human vision.Successfully resolve high image vein defections which simple color image restoration methods would be unable to process.

Future improvements include the modification of color components of image using wavelet transformation to achieve superior image inpainting results.THANK YOU