A.N. Hirani and T. Totsuka. Combining frequency and spatial domain information for fast interactive image noise removal. In SIGGRAPH 96 Conference Proceedings, Annual Conference Series, pages 269--276, August 1996.  Home/Search   Document Details and Download   Summary   Related Articles   Check

....recovery of the textured part of the image or to the recovery of its geometry. Several succesful algorithms exist for the recovery of textures. The basic idea in them is to select a texture (tipically modeled as a Markov random field) and synthesize it inside the region to be filled in (the hole) [40, 39, 32, 66]. The recovery of the geometric part of the image in a hole, or region where the data is missing, was first formulated by S. Masnou and J.M. Morel [54] as a variational problem, trying to interpolate the data in the hole. They were inspired by the work of D. Mumford, M. Nitzberg and T. Shiota [59] ....

A. Hirani and T. Totsuka, Combining frequency and spatial domain information for fast interactive image noise removal, Computer Graphics, pp. 269-276, SIGGRAPH 96, 1996.

....since consecutive frames do not provide new information. This is also the case when the region to be inpainted occupies a large number of frames. Another area related to our work is texture synthesis, in which a texture is selected and synthesized inside the region to be filled in (the hole) [9, 13, 15, 34]. These algorithms often require the user to select the texture and are not often well designed to fill in structure from boundary data. The closest methods to our approach are the fundamental works on disocclusion and line continuation. A pioneering contribution in this area is described in ....

A. Hirani and T. Totsuka. Combining Frequency and spatial domain information for fast interactive image noise removal. Computer Graphics, pp. 269-276, SIGGRAPH 96, 1996.

....is a photograph of a manual restoration of a fresco, taken from [31] 25 3.2 Three consecutive frames of a movie are shown. With the technique in [53] the inpainting is performed by using information in adjacent frames. 27 3. 3 With the technique in [39], the user selects what to put where. 27 3.4 With the technique in [59] straight lines are used to join points at the boundary which have equal graylevel. 28 3.5 Propagation direction as the normal to the signed ....

....models to interpolate losses in films from adjacent frames. The basic idea is to copy into the gap the right pixels from neighboring frames (see figure 3.2) The technique can not be applied to still images or to films where the regions to be inpainted span many frames. Hirani and Totsuka [39] combine frequency and spatial domain information in order to fill a given region with a selected texture. This is a very simple technique that produces incredibly good results. On the other hand, the algorithm mainly deals with texture synthesis (and not with structured background) and requires ....

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A. Hirani and T. Totsuka. Combining Frequency and spatial domain information for fast interactive image noise removal. Computer Graphics, pp. 269-276, SIGGRAPH 96, 1996.

....0. The work in [2] presents a formal variational approach that leads to a system of coupled second order differential equations. All these works were in part inspired by [21, 23] Full details can also be found at mountains.ece.umn.edu guille inpainting.htm. Additional related work is described in [7, 14, 15, 18, 19], while [6, 10, 17, 30] provides literature on inpainting as done by professional restorators. Comments on these contributions and comparisons with the work just described are provided in [5] 5 Experimental results We now present additional experimental results and compare with the case when ....

A. Hirani and T. Totsuka, "Combining frequency and spatial domain information for fast interactive image noise removal," Computer Graphics, pp. 269-276, SIGGRAPH 96, 1996.

....references concerning to the image retouching, see the pioneering work of Bertalmio et al. 1] which describes an image inpainting (non texture) algorithm based on partial differential equations. The technique to fill a given region with a selected texture was presented by Hirani and Totsuka in [2]. See also the recent studies [3] 4] and [5] In this paper we show that compactly supported radialbasis functions (CSRBFs) 6] offer a mechanism for obtaining extrapolated points of a damaged image, and, in fact, exhibit nice restoration results in image retouching examples. We consider 3D ....

....community. Let us point out here that we clearly understand that every technique has some strengths and some shortcomings. The main shortcoming of the proposed technique is that the technique will fail with retouching of synthetic examples (curve restoration) such as that in Figure 4 of reference [2]. Nevertheless, in practical applications dealing with real images, the possibility to use different levels or separated fragments of images that was realized in an Adobe Photoshop plug in (developed by our group and available for download from Web [7] allows us to avoid intermixing different ....

[Article contains additional citation context not shown here]

A.N. Hirani, and T. Totsuka, Combining frequency and spatial domain information for fast interactive image noise removal, SIGGRAPH 96, 269-276, 1996.

....Gracchi by J. Suvee (Louvre) This is a photograph of a manual restoration of a fresco, taken from [31] 29 3.2 Three consecutive frames of a movie are shown. With the technique in [53] the inpainting is performed by using information in adjacent frames. 31 3. 3 With the technique in [39], the user selects what to put where. 32 3.4 With the technique in [59] straight lines are used to join points at the boundary which have equal graylevel. 33 vii 3.5 Propagation direction as the normal to the signed distance to the boundary ....

....models to interpolate losses in films from adjacent frames. The basic idea is to copy into the gap the right pixels from neighboring frames (see figure 3.2) The technique can not be applied to still images or to films where the regions to be inpainted span many frames. Hirani and Totsuka [39] combine frequency and spatial domain information in order to fill a given region with a selected texture. This is a very simple technique that produces incredibly good results. On the other hand, the algorithm mainly deals with texture synthesis (and not with structured background) and requires ....

[Article contains additional citation context not shown here]

A. Hirani and T. Totsuka. Combining Frequency and spatial domain information for fast interactive image noise removal. Computer Graphics, pp. 269-276, SIGGRAPH 96, 1996.

....much better result can be generated by using a modification of the algorithm with 2 passes. film frame, or simply an undesirable object in an image. Since the processes causing these flaws are often irreversible, an algorithm that can fix these flaws is desirable. For example, Hirani and Totsuka [10] developed an interactive algorithm that finds translationally similar regions for noise removal. Often, the flawed portion is contained within a region of texture, and can be replaced by constrained texture synthesis [6, 11] Texture replacement by constrained synthesis must satisfy two ....

A. N. Hirani and T. Totsuka. Combining frequency and spatial domain information for fast interactive image noise removal. Computer Graphics, 30(Annual Conference Series):269--276, 1996.

....dealing with still images. Another area related to the work here described is texture synthesis. The basic idea here is to select a texture and synthesize it inside the region to be lled in (the hole) Although outstanding texture synthesis results have been reported in the literature, e.g. [22, 14, 19, 36], these algorithms require the user to select the texture to be copied into the hole. For images where the region to be replaced covers several di erent structures, the user would need to go through the tremendous work of segmenting them and searching corresponding replacements throughout the ....

A. Hirani and T. Totsuka. \Combining frequency and spatial domain information for fast interactive image noise removal," Computer Graphics, pp. 269-276, SIGGRAPH 96, 1996.

....images or static scenes. Another area related to the work here described is texture synthesis. The basic idea here is to select a texture and synthesize it inside the region to be filled in (the hole) Although outstanding texture synthesis results have been reported in the literature, e.g. [11, 7, 10, 19], these algorithms require the user to select the texture to be copied into the hole. For images where the region to be replaced covers several different structures, the user would need to go through the tremendous work of segmenting them and searching corresponding replacements throughout the ....

A. Hirani and T. Totsuka. "Combining frequency and spatial domain information for fast interactive image noise removal," Computer Graphics, pp. 269-276, SIGGRAPH 96, 1996.

....artifacts at the boundary between original and synthesized pixels, whereas convergence to the desired pixels within the mask support region is achieved almost perfectly. This technique is applicable to the restoration of pictures which have been destroyed in some subregion ( filling holes ) e.g. Hirani and Totsuka, 1996), although the estimation of parameters from the defective image is not straightforward. Figure 19 shows a set of examples that have been spatially extrapolated using this method. Observe that the border between real and synthetic data is barely noticeable. An additional potential benefit is that ....

Hirani, A.N. and Totsuka, T. 1996. Combining frequency and spatial domain information for fast interactive image noise removal. In ACM SIGGRAPH, pp. 269--276.

....the resulting image onto the set of images having the desired sample moments and range. Figure 10 shows a block diagram of this synthesis by analysis algorithm. The algorithm bears a close resemblance to the projection onto convex sets (POCS) approaches that have been used in image restoration [65, 37], and to the texture synthesis method of Heeger and Bergen [36] 12 In general, it is desirable to implement the projection operations so as to select the image in the constraint set that is closest (in a Euclidean sense ) to the initial image. Assuming the constraint surface is continuous, the ....

A N Hirani and T Totsuka. Combining frequency and spatial domain information for fast interactive image noise removal. In ACM SIGGRAPH, pages 269-276, 1996.

....here proposed, and not to be too dependent on the marking of the regions to be inpainted, we mark them in a very rough form with any available paintbrush software. Marking these regions in the examples reported in this paper just takes a few seconds to a non expert user. 2 Hirani and Totsuka [7] combine frequency and spatial domain information in order to fill a given region with a selected texture. This is a very simple technique that produces incredible good results. On the other hand, the algorithm mainly deals with texture synthesis (and not with structured background) and requires ....

A. Hirani and T. Totsuka. Combining Frequency and spatial domain information for fast interactive image noise removal. Computer Graphics, pp. 269-276, SIGGRAPH 96, 1996.

....1 Introduction Photographs and images often have regions which are in some sense flawed. At times, the flaw is due to a minute artifact: there may be scratches on the image, or perhaps a visible special effects wire. Fast interactive techniques exist for removing this type of small scale noise [5]. At other times, the flaw is present in a large region of the image: there may be a stain on the photograph, or some unsightly object may be present in the scene. One simplistic approach is to copy and blend in a similar region from somewhere else in the image. This approach has two major ....

A. Hirani and T. Totsuka, "Combining Frequency and Spatial Domain Information for Fast Interactive Noise Removal". In Computer Graphics (SIGGRAPH '96 Proceedings) , volume 30, pages 269-276, 1996.

....the resulting image onto the set of images having the desired sample moments and range. Figure 10 shows a block diagram of this synthesisby analysis algorithm. The algorithm bears a close resemblance to the projection onto convex sets (POCS) approaches that have been used in image restoration [66, 37], and to the texture synthesis method of Heeger and Bergen [36] In general, it is desirable to implement the projection operations so as to select the image in the constraint set that is closest (in a Euclidean sense ) to the initial image. Producing a minimal change in the image improves the ....

AN Hirani and T Totsuka. Combining frequency and spatial domain information for fast interactive image noise removal. In ACM SIGGRAPH, pages 269--276, 1996.

....etc. work only in one domain. In this paper we extend our previous algorithm to handle the difficult cases of shaded images, color images and movie sequences. 2 Previous Work A full survey of other noise removal algorithms and comparison of those with our POCS approach can be seen in [1] [2]. A prototype (sample) based approach for noise removal has to satisfy the following criteria. 1. Prominent lines must be made continuous. 2. Texture generated must match the surroundings. 3. Sample of different intensity should be acceptable. 4. Color images should be handled correctly. 5. Movie ....

....fail for movie sequences due to lack of inter frame coherence in reconstruction. 3 Still Image Algorithms 3.1 Base Algorithm Our previous algorithm BASIC solved the problems (1) and (2) mentioned above. BASIC consists of the three convex projections P min , P real , and Prep . Refer to [2] for their definitions and proofs of their convexity 1 . 3.2 Soft Scratch Algorithm In images with texture that has many adjacent bright and dark areas, sometimes the transition between reconstructed pixels and background becomes slightly noticeable. To solve this, we created a new algorithm ....

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Hirani, A.N. and T. Totsuka, "Combining Frequency and Spatial Domain Information for Fast Interactive Image Noise Removal", Computer Graphics Proc. (SIGGRAPH96), pp.269-276, 1996.

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A.N. Hirani and T. Totsuka. Combining frequency and spatial domain information for fast interactive image noise removal. In SIGGRAPH 96 Conference Proceedings, Annual Conference Series, pages 269--276, August 1996.

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A. N. Hirani and T. Totsuka, Combining Frequency And Spatial Domain Information For Fast Interactive Image Noise Removal, SIGGRAPH '96, pp. 269--276, 1996.

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A. Hirani and T. Totsuka, "Combining frequency and spatial domain information for fast interactive image noise removal," in Computer Graphics, SIGGRAPH 96, 1996, pp. 269--276.

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A. Hirani and T. Totsuka, "Combining frequency and spatial domain information for fast interactive image noise removal," Computer Graphics, pp. 269-276, SIGGRAPH 96, 1996.

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A. Hirani and T. Totsuka, "Combining Frequency and Spatial Domain Information for Fast Interactive Image Noise Removal", Proc. SIGGRAPH'96 Conf., pp. 269-276, 1996.

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A N Hirani and T Totsuka. Combining frequency and spatial domain information for fast interactive image noise removal. In ACM SIGGRAPH, pages 269-276, 1996.

No context found.

A N Hirani and T Totsuka. Combining frequency and spatial domain information for fast interactive image noise removal. In ACM SIGGRAPH, pages 269--276, 1996.

No context found.

A. N. Hirani and T. Totsuka. Combining frequency and spatial domain information for fast interactive image noise removal. In ACM SIGGRAPH, pp 269--276, 1996.

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