Linear Gaussian Method to Detect Blurry Digital Images using SIFT

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1 IJCAES ISSN: Volume III, Special Issue, November 2013 International Journal of Computer Applications in Engineering Sciences Special Issue on Emerging Research Areas in Computing(ERAC) Linear Gaussian Method to Detect Blurry Digital Images using SIFT Rupali Yashwant Landge #, Rakesh Sharma * Department of Computer Science and Engineering, Rajasthan College of Engineering for Women, Jaipur, Rajasthan, India 1 rupalilandge@gmail.com 2 mtech@rcew.ac.in Abstract Digital photos are massively produced while digital cameras are becoming popular; however, not every photo has good quality. Blur is one of the conventional image quality degradation which is caused by various factors like limited contrast; inappropriate exposure time and improper device handling. Blurry images make up a significant percentage of anyone's picture collections. So, an efficient tool is requiring for detecting blurry images and labelling or separating them for automatic deletion in order to preserve storage capacity. There are various methods to detect the blur from the blurry images some of which requires transforms like DCT or Wavelet and some don t require transform. A new technique is presented which automatically detect blurry images and separate them for later processing. The method will find out key points for both original and filtered image by using SIFT algorithm. After that calculate variance value for both the key points. Draw and analyse the plotted graph to determine whether the image is blurry or not. Keywords Blur, DCT, DWT, Harr-wavelet, SIFT. I. INTRODUCTION Advances in computational photography offers powerful low-cost digital cameras. This technology helps conventional users to generate high-quality content with inexpensive and bulky professional cameras. High-quality lenses and sensors are not only expensive but bulky and thus inappropriate for integration in small cameras and other devices such as mobile handsets. Computational photography offers highly efficient tools that can greatly improve the quality of pictures captured with low-quality lenses and sensors at very low cost. Simple factors such as limited light conditions, inappropriate exposure time and improper device handling can also cause unsatisfactory image quality. For that, the search for better image enhancement and selection tools in the field of computational photography goes on. As digital technology advances, high-quality digital cameras gain increasing attention. Users can take hundreds of pictures a day. However, it is not easy for them to look through all their pictures to decide which of them can be deleted, if the storage is full, or which of them should be taken for an enhancement process. Thus, some techniques of image quality estimation is need for separating the blurry images from the sharp ones. Imperfect focusing and/or motion is the main source of blurriness in digital photographs. Clearly, blurry images make up a significant percentage of anyone s picture collections captured with Conventional digital cameras. As a consequence, a tool to automatically detect blurry images is urgently needed. For that two reason are there. One is, blurry images can be labelled automatically and separated from good-quality images in conventional collections for browsing, viewing and later re-use. On the other hand, the same functionality can be used for automatic deletion in order to preserve storage capacity in the flash memory of a digital camera. The latter feature will enable users to virtually increase the storage capacity of their cameras by retaining only those pictures with perceptively good quality [1]. There are [186]

2 already some existing methods for blur detection or image quality estimation for digital images. However, most of them are time-consuming, computation intensive, need different kinds of transformations (e.g. DCT or DWT) or the detection ratio is not very high.also there is one new research algorithm for automatic real time detection of blurry images. The algorithm is based on computing variance values of the local key points that are extracted from the given image through implementing Scale Invariant Feature Transform (SIFT) algorithm in a scale space. No transforms (DCT or DWT) are required to be applied to the images, and no edge locations need to be identified in this method, which are the main techniques used in most of the existing methods. Only pixel values of the given images are directly employed in the algorithm [2]. II. RELATED WORK Blur is a form of bandwidth reduction of an ideal image owing to the imperfect image formation process. It can be caused by relative motion between the camera and the original scene, or by an optical system that is out of focus. Blur stands for smooth, lack of detail and sharpness. This in turn is equivalent to lack of high-frequency components in an image. Blurriness is unsatisfactory image quality. The four main causes of blurry photos are: Out Of Focus The subject moves while the shutter is open The camera moves while the shutter is open Depth Of Field is too shallow Blur is one of the conventional image quality degradation which is caused by various factors. The blurred images are further classified into either locally or globally blurred images. For globally blurred images, we estimate their point spread functions and classify them into camera shake or out of focus images. For locally blurred images, we find the blurred regions using a segmentation method, and the point spread function estimation on the blurred region can sort out the images with depth of field or moving object. The blur detection and classification processes are fully automatic and can help users to filter out blurred images before importing the photos into their digital photo albums. Three classes of blur detection techniques are exist. In the first class images are processes directly in the pixel domain. In this class most of the methods are based on edge detection method. The main drawback of this approach is, performance of the corresponding blur metrics strongly depends on a threshold used to classify the edges. The second class aims at post-processing the images stored in compressed form and conducts the corresponding analysis directly in the compressed domain. A representative of this class is presented in [3]. The compression standard is based on a DCT decorrelation transform and estimate blur by analysing the histogram of the DCT coefficients. Same technique is directly applicable in the JPEG or MPEG compressed images without decompression. The objective of blur detection in this application is to provide a percentage indicating the global image quality in terms of blur: 0% would mean that the frame is totally blurred while 100% would mean that no blur at all is present in that particular frame. This blur indicator characterizes the global image blur caused by camera motion or out of focus. Third class of techniques belongs to transform domain. Blur detection scheme using Harr wavelet transform is a direct methods. It can not only judge whether or not a given image is blurred, which is based on edge type analysis, but also determine to what extent the given image is blurred, which is based on edge sharpness analysis[4]. The scheme takes advantage of the ability of Harr wavelet transform in both discriminating different types of edges and recovering sharpness from the blurred version. It is effective for both Out-of-focus blur and Linear-motion blur. Its effectiveness will not be affected by the uniform background in images. Input Image Harr wavelet Edge Type Analysis Edge Sharpness Analysis Blurred or not? [187] Blur Extent

3 Fig..1. Structure of the HWT blur detection scheme Another simple method is also present which determine blur in digital images without using any transform. As a pre-processing, only converting the input images from RGB colours to grey-level luminance values is needed for the tool described below [2]. Following are the steps for above method Input image array as IMn Check if array is two dimensional or not, if not convert it Calculate global invariance value S2p for different sample values S2 p1 is sample variance value of the pre-image and S2 p2 is sample variance value of the taken image, if image is first one only i.e. if n=1 and S2 p1 = S2p, Go for the next image If not then calculate ratio R of sample variance values of pre-image and taken image. If R=1 or R< 1 then image is blur, if want to delete then delete it If not then image is not blur. III. RESEARCH METHOD In order to estimate images, first we apply SIFT algorithm, n below is, detecting local key points of the images objects. Then, generate additional images from the given one through the linear diffusion process. And finally, analyse the variance values calculated for the local key points of the original and its filtered images generated in the scale space. The algorithm for research method is described as follows: input an image apply SIFT to obtain key points of the image; randomly select a certain amount of the key points from the given image and fixed location; apply scale-space low-pass Gaussian filtering to the original image; take the key points in the filtered images from the same locations fixed previously in the original image; calculate variances for the taken key points in the original and filtered images and build the plot of it; Analyse the curvature given in the plot. The SIFT operator provides the number of key points found in the image and their position information. The number of key points varies from several hundreds to even hundreds of thousands per one image depending on the quality and structure of the image. To speed up the process and to minimize the time for calculation we are selecting m=300 number of key points by using random function and fixed their locations. After defining the values for key points in each scale space low pass filter image,we calculate variance values for each image. W= var= variance, w=weighted sum and n=number of iterations in scale space. The graph is plot by using var and n values. The behaviour of curve and weighted sum together will helps to determine whether image is blur or sharp. According to experiment it is found that if value of w<2 and number of key points are less than 3000 then image is not blur. The flow of algorithm is given below. Input image Apply SIFT Randomly select key points for the input image and fixed location Apply scale-space low-pass Gaussian filtering to the input image Take the same fixed key points for the filtered images [188] Calculate variances for the taken key points in the original and filtered images and build the plot of it;

4 Fig 2.flowchart of research method IV. RESULT To show, how research method work, we take one sample image and apply all steps of algorithm to same image. Fig. 3a.sample image Fig. 3b.gray conversion of sample image [189]

5 Fig. 3c.key point using SIFT algorithm Fig. 3d.keypoints for blur image using SIFT Fig. 3e.curve of variance value of selected key points According to the method, first stage is applying SIFT operator to the given image and fixing the initial selected key point positions. These key point positions are chosen randomly with uniform distribution. The original sample image is shown in fig a. The SIFT algorithm finds 577 key points for gray scale image as shown in fig.3c Next we [190]

6 start the process of image linear diffusion through the scale space. For this operation we apply the Gaussian low pass filter to the given image and process it. The example of this stage result is shown in fig 3d, which shows blur image along with 604 key points. After obtaining the sequence of linearly low-pass-filtered images we calculate the variances of their key points for image. The locations of selected key points had been fixed already in the first step after implementation of SIFT operator. Finally, we analyze the evolution of the curve built on the image values which indicates whether image is blur or sharp. (i.e. variance values of key points of consecutive images filtered in a scale space). To perform analysis, we weight the sum of differences between consecutive variance values by the maximum value of these variances. This is the final value of the original image based on which we estimate whether it is a low-quality blurred one or not. The graph for sample image is shown in fig 3e. The curve evolution for variances of the sharp image is smoother than that of the blurred one. In current experiment values of variances weighted sums are and for sharp and blur images, respectively. Such a difference is explained by the fact that the speed of curve smoothing is faster for a blurred image than for a sharp image. Therefore if the difference between subsequent variance values along the curve is low then the weighted sum of it will be higher. V. CONCLUSION In this paper, the main target of the research method is to detect blurry images and delete or separate them from digital camera storage, so that it will allow saving external memory and deleting unnecessary low-quality images. The method is reliable in terms of high accuracy and easy to implement. In order to reduce computational cost, only certain amount of local key points is used in the algorithm for image quality estimation, which allows using it for real-time applications. The only drawback of the method is that SIFT operator finds all the key points that is sometimes time consuming if the image is sharp. The same method can also extending our experiments for more detailed image evaluation, for example, to decide whether the image is partially blurry and how much of the image area is blurred. The method is applicable for real digital cameras. No additional pre-images are required. Only taken pictures should be processed in the camera. REFERENCES [1] Rupali Yashwant Landge,Rakesh Sharma Blur Detection Method sfor Digital Images-A survey International Journal of Computer Applications Technology and Research Volume 2 Issue 4, , 2013 [2] E. Tsomko H.J. Kim, E. Izquierdo Linear Gaussian blur evolution for detection of blurry images IET image process.,2010 Vol. 4,ISS.4pp [3] Prasad D.Pulekar Blur Detection in Digital Images-A Survey. [4] Tong H., Mingjing L., Hongjiang Z., Changshui Z.: Blur detection for digital images using wavelet transform. IEEE Int. Conf. on Multimedia and Expo (ICME), 2004, pp [5] X. Marichal, W. Y Ma, and H. J. Zhang, Blur determination in the compressed domain using DCT information, Proceedings of the IEEE International Conference on Image Processing, pp , [6] LOWE D.G.: Distinctive image features from scale invariant keypoints Int. J. Comput. Vis., 2004, 60, pp [7] VEDALDI A. An open implementation of SIFT detector and descriptor UCLA CSD Technical Report, , 2006 [8] ZHANG Q., CHEN Y., ZHANG Y., XU Y.: SIFT implementation and optimization for multi core systems IEEE Int. Symp. on Parallel and Distributed Processing, 2008, pp. 1 8 [9] LIU R., LI Z., JIA J.: Image partial blur detection and classification Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2008, pp. 1 8 [10] BOULT B.E., CHIANG M.C.: Local blur estimation and super- resolution Proc. IEEE Conf. Computer Vision and Pattern Recognition, 1997, pp [11] TICO M., TRIMECHE M., VEHVILAINEN M.: Motion blur identification based on differently exposed images IEEE Int. Conf. Image Processing, 2006, pp [12] TSOMKO E., KIM H.J.: Efficient method of detecting globally blurry or sharp images Proc. Ninth Int. Workshop on Image Analysis for Multimedia Interactive Services (WIAMIS), Klagenfurt, Austria, May 2008, pp [191]

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