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One of the most commonly used image processing tasks in photo editing is to improve the quality of the picture when it contains scratches, smudges or any unwanted object or drawing. The restoration method used to remove them is inpainting, which consists of filling in the areas, where the unwanted information is found, in an imperceptible way. Inpainting has been used since ancient times, where the concept of removing or replacing an object in an image has been developed through the painting of photographs. Recently, new inpainting techniques have emerged based on sparse models that offer a solution to this problem. A sparse model is a system of linear equations that involves the use of a dictionary together with a vector α to make the reconstruction or improvement of the images. Orthogonal Matching Pursuit (OMP) and K-SVD are the techniques used to obtain the dictionary and the vector α. These inpainting techniques provide fairly realistic results but have not been evaluated against other techniques. Therefore, in this work we compare the results obtained with sparse modelling against those obtained with two other techniques, the first one based on bilinear interpolation and the second one, called Isophote continuation, initially identifies the area to be reconstructed, then from the adjacent neighbours creates new layers within the region to be reconstructed and repeats the process until the area to be restored is completely filled. Initially, the results of the techniques were visually contrasted with the original images. Then the difference between the original and the resulting image was calculated taking into account only the areas of interest to find the number of non-zero pixels and the root mean square error (RMSE). The techniques based on dispersed models based generated good results as well as Inpainting by Continuation of Isophotes.
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