Platform motion blur is a common problem for airborne and space-based imagers. Photographs taken by hand or from moving vehicles in low-light conditions are also typically blurred. Correcting image motion blur poses a formidable problem since it requires a description of the blur in the form of the point spread function (PSF), which in general is dependent on spatial location within the image. ...

An analysis is presented for selecting the important rainbow-hologram formation-setup parameters for minimization of the image blur.

Single-image blind deblurring for imaging sensors in the Internet of Things (IoT) is a challenging ill-conditioned inverse problem, which requires regularization techniques to stabilize the image restoration process. The purpose is to recover the underlying blur kernel and latent sharp image from only one blurred image. Under many degraded imaging conditions, the blur kernel could be considered...

Image deblurring has matured over the last decade; today, there are a wide range of deblurring algorithms that operate successfully in the wild. Yet, there are many applications — including telephoto and low-light photography — where camera shake produces a blur kernel that is large enough to cripple state-of-the-art deblurring algorithms. This failure can be attributed to the decreasing SNR at...

Assessment for image quality traditionally needs its original image as a reference. The conventional method for assessment like Mean Square Error (MSE) or Peak Signal to Noise Ratio (PSNR) is invalid when there is no reference. In this paper, we present a new No-Reference (NR) assessment of image quality using blur and noise. The recent camera applications provide high quality images by help of...

The original solution of the blur and blur parameters identification problem is presented in this paper. A neural network based on multi-valued neurons is used for the blur and blur parameters identification. It is shown that using simple single-layered neural network it is possible to identify the type of the distorting operator. Four types of blur are considered: defocus, rectangular, motion ...

Blind blur identification in video sequences becomes more important. This paper presents a new method for identifying parameters of different blur kernels and image restoration in a weighted double regularized Bayesian learning approach. A proposed prior solution space includes dominant blur point spread functions as prior candidates for Bayesian estimation. The double cost functions are adjust...

This paper presents a novel approach to depth recovery from image blur by zooming. We first discuss the optical properties of a zoom lens system; the image blur varies according to zoom as well as focus and iris settings. Then a thin lens based camera model is proposed that is characterized by the effective focal length and lens diameter. Using this model, we have proved that the depth informat...

We observe that the effect of noise in image is a challenging issue in the current scenario. Image Denoising has remained a fundamental problem in the field of image processing. Many of the previous research use the basic noise reduction through image blurring. In this paper we survey and analysed different traditional image denoising method using different methods. We also suggest a new approa...