1/5/2024 0 Comments Ma2 multipatch![]() This patch-based approach is used primarily for noise reduction applications and exploits spatial redundancy that may express itself within an image, locally and/or non-locally. The key to this method lies in the particular correlation model used and how it is employed for spatially adaptive filtering.Ī different approach to image restoration, also relevant to the current paper, is based on fusing multiple similar patches within the observed image. Under certain conditions, the method can also be very computationally efficient. This AWF SR method has been shown to provide best-in-class performance for nonuniform interpolation-based SR and has also been used successfully for demosaicing and Nyquist sampled video restoration. The approach is also very well suited to dealing with non-uniformly sampled imagery and missing or bad pixels. This kind of AWF is capable of jointly addressing blur, noise, and undersampling and is well suited to dealing with a non-stationary signal and noise. This particular brand of AWF SR emerged from earlier work, including that in. The Wiener weights are determined based on a spatially varying spatial-domain parametric correlation model. This AWF approach employs a spatially varying weighted sum to form an estimate of each pixel. Recently, a form of adaptive Wiener filter (AWF) has been developed and successfully applied to super-resolution (SR) and other restoration applications by one of the current authors. While all of these methods may go by the name of ‘Wiener filter’, they can be quite different in their character. Rather, a pilot or prototype estimate is used in lieu of a parametric statistical model. In the case of the empirical Wiener filter, no explicit statistical model is used at all. Some statistical models are very simple, such as the popular constant noise-to-signal power spectral density model, and others are far more complex. Within each of these categories, a wide variety of statistical models may be employed. These include finite impulse response, infinite impulse response, transform-domain, and spatially adaptive methods. It is important to note that there are many disparate variations of Wiener filters. The standard Wiener filter is a linear space-invariant filter designed to minimize mean squared error (MSE) between the desired signal and estimate, assuming stationary random signals and noise. Many methods have focused exclusively on noise reduction, and others seek to address multiple degradations jointly, such as blur and noise.Ī widely used method for image restoration, relevant to the current paper, is the classic Wiener filter. A wide variety of linear and non-linear methods have been proposed. Restoring such degraded images is a fundamental problem in image processing that has been researched since the earliest days of digital images. These invariably include blurring from diffraction and noise from a variety of sources. The experimental results presented show that the proposed method delivers high performance in image restoration in a variety of scenarios.ĭuring image acquisition, images are subject to a variety of degradations. To the best of our knowledge, this is the first multi-patch algorithm to use a single spatial-domain weighted sum of all pixels within multiple similar patches to form its estimate and the first to use a spatial-domain multi-patch correlation model to determine the weights. Furthermore, it can also readily treat spatially varying signal and noise statistics. One key advantage of the CAWF approach, compared with many other patch-based algorithms, is that it can jointly handle blur and noise. The weights are based on a new multi-patch correlation model that takes into account each pixel’s spatial distance to the center of its corresponding patch, as well as the intensity vector distances among the similar patches. A single-stage weighted sum of all of the pixels in the similar patches is used to estimate the center pixel in the reference patch. We identify the most similar patches in the image within a given search window about the reference patch. At each position, the current observation window represents the reference patch. The CAWF employs a finite size moving window. The new filter structure is referred to as a collaborative adaptive Wiener filter (CAWF). We present a new patch-based image restoration algorithm using an adaptive Wiener filter (AWF) with a novel spatial-domain multi-patch correlation model.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |