| The process of image restoration is to use an algorithm to repair or reconstruct the lack of information,it makes person would not detect obvious repair trails.The current image repair technology has been widely applied in the protection of cultural relics,repairing old photos,removing excess areas and others.Firstly this paper studies the existing classic image restoration algorithm,through the algorithm simulation,data contrast and the analysis of the results,also compared the advantages and weaknesses of classical algorithm of color image repairing effect respectively.And then on the basis of image sparse restoration algorithm,it takes some update measures on the existing method and studies the color image restoration method based on sparse representation1.Since the correlation and the complexity structure of RGB color model leads to the shortcomings on non-ideal effect of the color image restoration process,the thesis proposes a new sparse representation color inpainting method to improve the repair effect.Firstly,the RGB color image is mapped into YUV model and divided parts.Then useful information is extracted from the volume of natural and undamaged images.Using the Fast-ICA algorithm to obtain a complete dictionary;at last priority function is used to determine to repair the turn to be repaired,combining with reconstruction algorithm SL0 to treat repair piece of reconfiguration and repaint.Experimental results show that proposed method can effectively repair the strip breakage,small damage,large pieces of broken images and the text removed,and repairing boundary and the texture is more conformed to the human eye vision effect.2.In the area of image restoration algorithms represented by conventional sparse,the size of the dictionary atomic is fixed.There will be excessive blurring caused by the fixed dictionary atom when repairing the texture region,and influence the image restoration effect when repairing the smooth region due to the dictionary atom is too small to extended region.To solve this weakness,the thesis proposes an adaptive dictionary atom-size determining method through analysis the structure information on texture,edge,smooth and other areas,which would confirm the size of dictionary atomic.The simulation results show that the proposed algorithm can effectively work out the problem of details could be blurred in a fixed dictionary repair,and other shortcomings such as region extending while the effect of image restoration is significantly improved. |
