In practice, we meet numerous image restoration problems, particularly those on face images. An approach to image restoration based on statistical prior of faces and image blur measure is presented in this paper. The novelty of the approach is twofold: 1) the prior on the shape and appearance of a face represented as statistical models is incorporated into the regularized image restoration form...

The degree of image degradation due to atmospheric turbulence is particularly severe when imaging over long horizontal paths since the turbulence is strongest close to the ground. The most pronounced effects include image blurring and image dancing and in case of strong turbulence image distortion as well. To mitigate these effects a number of methods from the field of image processing have bee...

Blind image restoration is a kind method of image processing by estimating both the original image and the point spread function from degraded images, when there is unknown or not completely know the prior knowledge of the original image and point spread function of imaging. However, blind image restoration is a complex and intensive computing process, and increasing processing speed has been a...

The purpose of this exercise is to help you understand the concept of the frequency domain by doing some manipulation of images. The exercise will have 4 parts : first two parts on basic manipulation of images in the frequency domain and the second two parts are about image restoration: overcoming blur in images. The last part of the exercise (Inverse-Filtering in the spatial domain) is optiona...

Despite its practical importance in image processing and computer vision, blind blur identification and blind image restoration have so far been addressed under restrictive assumptions such as all-pole stationary image models blurred by zero or minimum-phase point-spread functions. Relying upon diversity (availability of a sufficient number of multiple blurred images), we develop blind FIR blur...

Image restoration is a systematic process that regains the lost clarity of an image. In the past, image restoration based on sparse representation has resulted in better performance for natural images. Within each category of image restoration such as de-blurring, de-noising and super resolution, different algorithms are selected for evaluation and comparison. It is evident that both local and ...

Image Restoration is the method of recovering original image from degraded image and also to understand the image without any artifact errors. Image restoration methods can be considered as direct and indirect techniques. Direct techniques are used when restoration results are generated in a simple one step fashion. Similarly, indirect techniques are used when restoration results are obtained a...

Purpose Healthcare providers (HCP) who care for traumatically injured service members are at risk for stress, burnout, and decreased clinical effectiveness. After two decades of war, the intensive work environment and stress associated with caring for these service members and their families are damaging the emotional and physical wellbeing of our military HCPs. This pilot study explored the ef...

Total variation (TV) minimizing image restoration is a fairly new approach to image restoration, and has been shown both analytically and empirically to be quite eeective. Our primary concern here is to develop a spatially adaptive TV minimizing restoration scheme. One way of accomplishing this is to locally weight the measure or computation of the total variation of the image. The weighting fa...

Classical linear image restoration techniques assume that the linear shift invariant blur, also known as the point-spread function (PSF), is known prior to restoration. In many practical situations, however, the PSF is unknown and the problem of image restoration involves the simultaneous identi cation of the true image and PSF from the degraded observation. Such a process is referred to as bli...