A Study on Digital Image Restoration Filters

In this paper, at first a color image is taken Then the image is transformed into a grayscale image. After that, the motion blurring effect is applied to that image according to the image degradation model described in equation 3.the blurring effect can be controlled by a and b components of the model. Then random noise is added in the image via MATLAB programming. Many methods can restore the noisy and motion blurred image: particularly in this paper inverse filtering as well as wiener filtering are implemented for the restoration purpose consequently, both motion blurred and noisy motion blurred image are restored via inverse filtering as well as wiener filtering techniques and the comparison is made among them.

A Low-Light Image Enhancement Method Based on Image Degradation Model and Pure Pixel Ratio Prior

Images captured in low-light conditions are prone to suffer from low visibility, which may further degrade the performance of most computational photography and computer vision applications. In this paper, we propose a low-light image degradation model derived from the atmospheric scattering model, which is simple but effective and robust. Then, we present a physically valid image prior named pure pixel ratio prior, which is a statistical regularity of extensive nature clear images. Based on the proposed model and the image prior, a corresponding low-light image enhancement method is also presented. In this method, we first segment the input image into scenes according to the brightness similarity and utilize a high-efficiency scene-based transmission estimation strategy rather than the traditional per-pixel fashion. Next, we refine the rough transmission map, by using a total variation smooth operator, and obtain the enhanced image accordingly. Experiments on a number of challenging nature low-light images verify the effectiveness and robustness of the proposed model, and the corresponding method can show its superiority over several state of the arts.

The Dedusting Method Based on a Single Still Image

In order to restore the image in dust environment, a method was proposed to restore the images in dust environment based on a single still image. Firstly, an image degradation model considering multiple scattering factors caused by dust was built using the first-order multiple scattering methods. Then, a new geometric depth model of single image was presented. This model utilized depth map of a dust image to improve usual geometric depth model. According to the new model the every distance of the objects in dust environment was able to acquire from a single still image. Finally, an image in dust environment could be restored according to the relationship between the dark channel prior model and the distance information which was calculated by the geometric depth model. The experimental results have shown that the method enhanced luminance and contrast. The method provided a foundation for target recognition in the dust environments.

Fast Restoration Algorithm for Rotational Motion Blurred Images

This paper first introduces the history and research status of motion-blurred image restoration, and then establishes a rotating image degradation model. we present a fast algorithm for real-time hardware restoration of rotating motion blurred images, the algorithm is simulated and experimental platform is built for the actual verification. Through comparison and analysis of the recovery results, we verify the accuracy of the algorithm, and it shows that this rotational motion blurred image restoration algorithm can receive satisfactory results.