Low light level image target detection based on texture saliency

As a major branch and research focus in the image processing field, the study on image restoration is always of practical significance and application value. Blind image restoration has always been one of the most difficult problems in image restoration. In view of the image motion blurring induced by the relative motion between the camera and the subject, we present a blind image restoration method based on guided filter. We first estimate the point spread function by using the iteration algorithm in the frequency domain. And then, because the guided filter has the edge-preserving smoothing property, we restore the target image by the non-blind image restoration algorithm based on the guided filter. By iterating the above two steps, we can obtain the original clear image. In order to verify the effectiveness of the proposed algorithm, we give several groups of experiments. Experimental results show that the proposed algorithm can not only effectively eliminate the noise and suppress the ringing, but also well preserve the edge and texture details. Therefore, the proposed algorithm can restore the image with high quality.

Guided filter-based blind image restoration method

Xin-nan¹,

Huang²,

Jia³

et al. 2015

Complex background model and foreground detection based on random aggregation

Guo-Ling¹,

Xu²,

Chen³

et al. 2015

In order to build a robust background model and improve the accuracy of the foreground object detection, we give a comprehensive consideration on the same location pixels of the relevance of time and the correlation of space with its adjacent pixels; and based on the classic ViBe of random algorithm ideas, a kind of complex background model and foreground detection method is proposed. Using the first n series of images to initialize the background model with the sample consistency principle, we can avoid the appearance of the “Ghost” phenomenon; and get the difference between each pixel and its multiple sample value in the background model, and then compute the sum and the average. The average shows the dynamic degree of the background point which is the corresponding pixel background of dynamic feedback information. We get the adaptive clustering threshold and adaptive updating threshold with the dynamic feedback to make random clusters realize the adaptability to dynamic background and combine the global disturbance threshold with the local pixel level judgment threshold to implement the immunity of illumination with slow changes, fast changes or sudden changes, so that we can segment the prospect target accurately. By selecting neighborhood pixels to update the neighborhood background randomly in terms of spatial information dissemination mechanism, a good detection effect is obtained in the case of camera shake. Through multiple sets of test data, experimental results show that this algorithm can significantly improve the adaptability and robustness of the background model such as dynamic backgrounds, illumination changes, and camera shake. The algorithm can well apply to the occasion of moving targets in infrared image detection, and expand its application range. Without any image preprocessing and morphological post-processing, the original detection accuracy of foreground is superior to other algorithms.

Fast Bayesian blind restoration for single defocus image with iterative joint bilateral filters

Yin¹,

Wang²,

Wang³

et al. 2016

It is significant to realize effective defocus image restoration for acquiring clear image in military and geological examination field. Most of existing algorithms have the problems of large computational cost, ringing and noise sensitivity, hence a novel approach by iterative joint bilateral filtering under Bayesian framework is proposed. Firstly, it utilizes defocus image depth estimation to compute the point spread function in the Bayesian framework. Then a minimum optimization problem is built to represent the blind restoration problem. After inferencing the solution procedure of the minimum optimization problem, we find that the joint bilateral filters can be used to search the optimal solution, which not only simplifies the searching procedure but also reduces the computational cost. Finally, an iterative joint bilateral filtering is designed to realize the image restoration. That means that the original restored image obtained from the bilateral filtering is used to design the guide image for the joint bilateral filters, and the guide image will serve as the input of the optimization problem for acquiring the better optimal result. This procedure is repeated until convergence. The experimental results indicate that this method can yield the ringing, reduce the computational cost, and remove the noise. Generally speaking, the average pixel error of 85% images is under 0.03, which has improved 19% comparing with the same error rang of existing algorithms, and 78% shorter than those of compared algorithms. It can be used in the engineering practice of blind restoration for single defocus image.