Low Depth Of Field Images Research Articles

A survey of image compression methods for low depth-of-field images and image sequences

The popularity of multimedia applications has resulted in development of lossless and lossy compression techniques. Many image compression methods clustered under these two compression techniques are discussed briefly in this article. In addition to this context, the survey paper gives a study of the recent algorithms that are available for coding low Depth-of-Field (DOF) images and also covers its extension for depth map image sequence coding. Motivation behind this work is to provide a detailed analysis of these algorithms such as the methodology used, merits and demerits, and the objective and subjective comparison of these algorithms with the standard compression algorithms like JPEG, JPEG 2000, H.261/AVC etc. Further, the paper concludes with a guideline for the new researchers in this field which concerns the design of an efficient compression method for low DOF images and depth map image sequences.

Towards better segmentation of object of interest using histogram equalisation and morphological reconstruction

This study investigates a segmentation algorithm with histogram equalisation and morphological reconstruction filters to extract meaningful Object of Interest (OOI) from the low depth of field images. Low Depth of Field is a popular photographic technique to focus the foreground and defocus the background. The proposed method is designed to enhance the contrast using histogram equalisation and extract the edge using edge detection. The method removes the holes and bright patches using the morphological reconstruction filters. After the filter process, the method merges the focused foreground objects from defocused background. The image analysis was conducted on 50 natural images using the proposed method. The results show that the accuracy is close to the manual segmentation. The accuracy of segmented image using the proposed method and manual segmentation was also compared with other state–of–art methods.

A Curve Evolution Approach for Unsupervised Segmentation of Images With Low Depth of Field

In this paper, we describe a novel algorithm for unsupervised segmentation of images with low depth of field (DOF). First of all, a multi-scale reblurring model is used to detect the object of interest (OOI) in saliency space. Then, to determine the boundary of OOI, an active contour model based on hybrid energy function is proposed. In this model, a global energy item related with the saliency map is adopted to find the global minimum, and a local energy term regarding the low DOF image is used to improve the segmentation precision. In addition, an adaptive parameter is attached to this model to balance the weight of global and local energy. Furthermore, an unsupervised curve initialization method is designed to reduce the number of evolution iterations. Finally, we conduct experiments on various low DOF images, and the results demonstrate the high robustness and precision of the proposed approach.

Region-of-interest extraction in low depth of field images using ensemble clustering and difference of Gaussian approaches

In this paper, a two-stage unsupervised segmentation approach based on ensemble clustering is proposed to extract the focused regions from low depth-of-field (DOF) images. The first stage is to cluster image blocks in a joint contrast-energy feature space into three constituent groups. To achieve this, we make use of a normal mixture-based model along with standard expectation-maximization (EM) algorithm at two consecutive levels of block size. To avoid the common problem of local optima experienced in many models, an ensemble EM clustering algorithm is proposed. As a result, relevant blocks closely conforming to image objects are extracted. In stage two, a binary saliency map is constructed from the relevant blocks at the pixel level, which is based on difference of Gaussian (DOG) and binarization methods. Then, a set of morphological operations is employed to create the region-of-interest (ROI) from the map. Experimental results demonstrate that the proposed approach achieves an F-measure of 91.3% and is computationally 3 times faster than the existing state-of-the-art approach.

Segmenting focused objects based on the Amplitude Decomposition Model

In this paper, we present an inherent model of the low depth-of-field (DOF) images, referred as the Amplitude Decomposition Model, which turns out to be useful for the detection and segmentation of focused objects in the low DOF images. By analyzing the low DOF image in frequency domain, the Amplitude Decomposition Model is firstly investigated, i.e., the amplitude spectrum of the low DOF image can be decomposed into the amplitude of its totally defocused version and the high-frequency difference amplitude of its focused regions. Based on this model, we propose a method for detecting focused objects. Using the detection result, we then utilize a thresholding method to segment the focused objects and employ the graph cut technique to refine the focused object boundary. Experimental results show that the proposed method can extract focused objects effectively and is comparable to the state-of-the-art methods.

Automatic segmentation of focused objects from images with low depth of field

In this paper, we propose an automatic segmentation approach to extract focused objects from images with low depth of field (DOF). A focus energy map is first estimated based on the difference of high-frequency components between focused region and defocused background, and is exploited to construct region/boundary saliency maps on the basis of a pre-segmentation result by watershed transform. Then region/boundary masks for focused object are generated by entropy thresholding and flood filling, and an efficient boundary linking method is proposed to obtain closed region/boundary masks, which are exploited to reasonably generate a trimap containing seed regions for focused object and defocused background, and uncertain regions, respectively. Finally, the trimap is used as the input to an image matting model, which is utilized to classify the pixels in the uncertain regions to obtain an accurate focused object segmentation result based on the estimated alpha matte. Experimental results for a variety of low DOF images demonstrate the good segmentation performance of the proposed approach.

Segmentation and region of interest based image retrieval in low depth of field observations

In this paper we address the problem of extracting the focused region and its use in retrieving similar images from a low depth of field image database. We compute the histogram of the local contrast at each pixel and model it as a mixture of two exponential distributions – one for the focused and the other for the defocused region. Unlike the mixture of Gaussian distributions, a mixture of exponential distributions overlaps with same monotonicity over the entire range in [0, ∞) and it is difficult to separate into components. We estimate the parameters of these distributions using the EM algorithm. This is followed by a hypothesis testing which segments the focused region in the low depth of field image. A content-based retrieval scheme is now confined to the detected region for a proper retrieval. Experimental results for both segmentation and image retrieval using a database consisting of 4986 images are presented to show the efficacy of the suggested scheme.

Unsupervised multiresolution segmentation for images with low depth of field

Unsupervised segmentation of images with low depth of field (DOF) is highly useful in various applications. This paper describes a novel multiresolution image segmentation algorithm for low DOF images. The algorithm is designed to separate a sharply focused object-of-interest from other foreground or background objects. The algorithm is fully automatic in that all parameters are image independent. A multi-scale approach based on high frequency wavelet coefficients and their statistics is used to perform context-dependent classification of individual blocks of the image. Unlike other edge-based approaches, our algorithm does not rely on the process of connecting object boundaries. The algorithm has achieved high accuracy when tested on more than 100 low DOF images, many with inhomogeneous foreground or background distractions. Compared with he state of the art algorithms, this new algorithm provides better accuracy at higher speed.