For the calculation of inpaint pixels replacements sometimes are used gradient based methods, the most well-known of these is the Navier-Stokes method, described in the paper "Navier-Stokes, Fluid Dynamics, and Image and Video Inpainting", by Bertalmio, Marcelo, Andrea L. MagicInpainter 3.0 uses inpaint filters to find the best replacement candidates for the noisy pixels. Using larger inpaint radius means that pixels further away will participate in the reconstruction but just increasing R does not always means better quality. Usually this is called filter kernel radius and is given in pixels. Inpaint Radius This radius controls the size of the area around each noise pixel, it would be used from the inpaint filter. To resolve this MagicInpainter provides the Zoom In and Zoom Out buttons and one parameter called Max. It makes it more difficult and too computationally expensive to find a best match. Zoom In/Out For images with higher resolution generated feature keys are too many, This parameter is given in percents and controls which feature keys from the low noise area can be used during the inpaint, valid image keys are these for which is fullfiled: In some cases pixels close to the image edges or with too many noise around can distort results. Choosing the objects for removal is done manually with applying a mask, MagicInpainter 3.0 has Eraser Button allowing to apply such mask over any part of the image. We first separate image into two regions – low noise and high noise, then we take pixels from the low noise area and fills in the pixels from the high noise area going from outside to inside. Some of the parameters of the inpaint (available in Settings): So the process of extracting image keys can be called constructing the feature space and inpaint is then can be called the process of decreasing image noise using this feature space. Thus, like many other problems for inpaint we use a feature space composed from the collected before valid image keys. Image keys are extracted from the non-noisy pixels neighborhood and then used to find the best match for the corresponding noisy pixels. This is done with assigning each pixel or group of several pixels to the so-called image keys or as some researchers call them image features. Inpaint is done only with extracting valid image data from the image area with low noise and then using this data to fill in areas with high noise. Training, preprocessing or additional images are not necessary. MagicInpainter 3.0 fills in the pixels from the missing noisy regions using the data from the undamaged area in the same image, also called low noise area. Below I have shown that while in certain cases, especially when reconstructing complex features, AI methods clearly have the edge, for others like reconstruction and generation of textures the traditional inpaint methods are still more reliable. MagicInpainter 3.0 uses the first group of methods. (called also AI methods)īoth approaches have pros and cons for different areas of application. Deep learning using neural networks with CNNs, etc.Image processing algorithms (called also traditional).There are two main groups of modern image inpaint methods: Inpaint (or image reconstruction) is a process of filling in the missing parts of images in a natural way, preserving textures and background so that a person is not able to spot the difference. Starting with version 3.0 GPU optimized image inpaint algorithms are used. MagicInpainter 3.0 is image processing tool for inpaint, mask selection and objects removal of small and medium sized objects from photos.
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