Besides a high distinctiveness, robustness (or invariance) to image degradations is very desirable for texture feature extraction methods in real-world applications. In this paper, focus is on making arbitrary texture descriptors invariant to blur which is often prevalent in real image data. From previous work, we know that most state-of-the-art texture feature extraction methods are unable to ...

The goal of image restoration is to improve a given image in some predefined sense. Restoration attempts to recover an image by modelling the degradation function and applying the inverse process. Motion blur is a common type of degradation which is caused by the relative motion between an object and camera. Motion blur can be modeled by a point spread function consists of two parameters angle ...

Variations in blur are present in retinal images of scenes containing objects at multiple depth planes. Here we examine whether neural representations of image blur can be recalibrated as a function of depth. Participants were exposed to textured images whose blur changed with depth in a novel manner. For one group of participants, image blur increased as the images moved closer; for the other ...

Visibility in poor weather condition is severely degraded by scattering of light due to suspended particles in the atmosphere such as haze and fog. In this paper, we propose defogging method from a single image based on depth estimation using blur. Formation of fog is the function of the depth. Estimation of depth information is under constraint problem if single image is available. Hence, remo...

A fundamental problem in conventional photography is that movement of the camera or captured object causes motion blur in the image. In this research, we propose coding motion-invariant blur using a programmable aperture camera. The camera realizes virtual camera motion by translating the opening, and as a result, we obtain a coded image in which motion blur is invariant with respect to object ...

In our CVPR paper “Gyro-based Multi-Image Deconvolution for Removing Handshake Blur” [4], we proposed a multi-image deblurring system that uses gyroscope data for camera motion estimation. Although our method effectively removes handshake blur in static regions, moving objects and over-exposed regions are not handled properly. These regions are considered as outliers because they do not follow ...

In pattern recognition problems, the effectiveness of the analysis depends heavily on the quality of the image to be processed. This image may be blurred and/or noisy and the goal of digital image restoration is to find an estimate of the original image. A fundamental issue in this process is the blur estimation. When the blur is not readily avalaible, it has to be estimated from the observed i...

To achieve the best image quality, noise and artifacts are generally removed at the cost of a loss of details generating the blur effect. To control and quantify the emergence of the blur effect, blur metrics have already been proposed in the literature. By associating the blur effect with the edge spreading, these metrics are sensitive not only to the threshold choice to classify the edge, but...

We address the problem of space-variant image deblurring, where different parts of the image are blurred by different blur kernels. Assuming a region-wise space variant point spread function, we first solve the problem for the case of known blur kernels and known boundaries between the different blur regions in the image. We then generalize the method to the challenging case of unknown boundari...

Blind image restoration algorithms for motion blur have been deeply researched in the past years. Although great progress has been made, blurred images containing large blur and rich, small details still cannot be restored perfectly. To deal with these problems, we present a robust image restoration algorithm for motion blur of general image sensors in this paper. Firstly, we propose a self-ada...