Ali Mosleh

Image Text Detection Using a Bandlet-Based Edge Detector and Stroke Width Transform.

In this paper, we propose a text detection method based on a feature vector generated from connected components produced via the stroke width transform. Several properties, such as variant directionality of gradient of text edges, high contrast with background, and geometric properties of text components jointly with the properties found by the stroke width transform are considered in the formation of feature vectors. Then, k-means clustering is performed by employing the feature vectors in a bid to distinguish text and non-text components. Finally, the obtained text components are grouped and the remaining components are discarded. Since the stroke width transform relies on a precise edge detection scheme, we introduce a novel bandlet-based edge detector which is quite effective at obtaining text edges in images while dismissing noisy and foliage edges. Our experimental results indicate a high performance for the proposed method and the effectiveness of our proposed edge detector for text localization purposes.

Automatic Inpainting Scheme for Video Text Detection and Removal

We present a two stage framework for automatic video text removal to detect and remove embedded video texts and fill-in their remaining regions by appropriate data. In the video text detection stage, text locations in each frame are found via an unsupervised clustering performed on the connected components produced by the stroke width transform (SWT). Since SWT needs an accurate edge map, we develop a novel edge detector which benefits from the geometric features revealed by the bandlet transform. Next, the motion patterns of the text objects of each frame are analyzed to localize video texts. The detected video text regions are removed, then the video is restored by an inpainting scheme. The proposed video inpainting approach applies spatio-temporal geometric flows extracted by bandlets to reconstruct the missing data. A 3D volume regularization algorithm, which takes advantage of bandlet bases in exploiting the anisotropic regularities, is introduced to carry out the inpainting task. The method does not need extra processes to satisfy visual consistency. The experimental results demonstrate the effectiveness of both our proposed video text detection approach and the video completion technique, and consequently the entire automatic video text removal and restoration process.

Camera intrinsic blur kernel estimation: A reliable framework

This paper presents a reliable non-blind method to measure intrinsic lens blur. We first introduce an accurate camera-scene alignment framework that avoids erroneous homography estimation and camera tone curve estimation. This alignment is used to generate a sharp correspondence of a target pattern captured by the camera. Second, we introduce a Point Spread Function (PSF) estimation approach where information about the frequency spectrum of the target image is taken into account. As a result of these steps and the ability to use multiple target images in this framework, we achieve a PSF estimation method robust against noise and suitable for mobile devices. Experimental results show that the proposed method results in PSFs with more than 10 dB higher accuracy in noisy conditions compared with the PSFs generated using state-of-the-art techniques.

Image Deconvolution Ringing Artifact Detection and Removal via PSF Frequency Analysis

We present a new method to detect and remove ringing artifacts produced by the deconvolution process in image deblurring techniques. The method takes into account non-invertible frequency components of the blur kernel used in the deconvolution. Efficient Gabor wavelets are produced for each non-invertible frequency and applied on the deblurred image to generate a set of filter responses that reveal existing ringing artifacts. The set of Gabor filters is then employed in a regularization scheme to remove the corresponding artifacts from the deblurred image. The regularization scheme minimizes the responses of the reconstructed image to these Gabor filters through an alternating algorithm in order to suppress the artifacts. As a result of these steps we are able to significantly enhance the quality of the deblurred images produced by deconvolution algorithms. Our numerical evaluations using a ringing artifact metric indicate the effectiveness of the proposed deringing method.

A video completion method based on bandlet transform

We present a bandlet transform-based technique to complete missing parts of a video sequence captured with a static camera. The method is followed by a preprocessing step to separate the foreground which consists of moving objects and background frames in order to facilitate the video completion task. The technique considers two cases: 1) filling-in the background frames after removing objects, and 2) fillingin the occluded parts of a moving object. In the first case, a precise optimization in the bandlet domain is performed to complete the background video. In the second case, a priority based exemplar algorithm, which applies bandlet geometry properties, is used to fill-in the occluded moving object. Our experimental results indicate the effectiveness of the proposed video completion technique.

Compressed domain JPEG2000 image indexing method employing full packet header information

In this paper we propose a new indexing method for comparing image contents in the JPEG2000 compressed domain. Comparing image contents of the JPEG2000 coded images in the pixel domain requires image decompression, which imposes intensive computational processes of the inverse discrete wavelet transform and the arithmetic decoding. On the other hand, the first decoding stage of the JPEG2000 standard is packet header decoding, which is a simple process, but provides valuable information about the code blocks in the packet. In this paper we use packet header information for JPEG2000 image indexing. The proposed method exploits full packet header information including the number of non-zero bit-planes, the number of coding passes and the code block length for indexing the JPEG2000 compressed images. Experimental results show that the proposed method provides a better performance compared to other JPEG2000 compressed domain indexing techniques and even outperforms the pixel-based image indexing techniques such as the Gabor filter.

Bandlet-based sparsity regularization in video inpainting

We present a bandlet-based framework for video inpainting in order to complete missing parts of a video sequence. The framework applies spatio-temporal geometric flows extracted by bandlets to reconstruct the missing data. First, a priority-based exemplar scheme enhanced by a bandlet-based patch fusion generates a preliminary inpainting result. Then, the inpainting task is completed by a 3D volume regularization algorithm which takes advantage of bandlet bases in exploiting the anisotropic regularities. The method does not need extra processes in order to satisfy visual consistency. The experimental results demonstrate the effectiveness of our proposed video completion technique.

Image and video spatial super-resolution via bandlet-based sparsity regularization and structure tensor

A new framework for single image and video super-resolution is proposed in this paper. The main idea is to employ the geometric details of the image in a way that minimizes the possible artifacts caused by the super-resolution process. To this end, we benefit from the bandlet transform which captures the image surface geometry quite effectively. The proposed single image super-resolution method is a two-stage scheme. In the first step, edges and high frequency details of the image are interpolated in the enlarged image. This interpolation uses a structure tensor, which is modified according to the geometry layer revealed by the image bandlets. In the second stage, the edge interpolated image is fed to an inpainting scheme to estimate the pixel values for the new spatial locations within the enlarged image. These locations are treated as missing pixels and then a precise regularization in the bandlet domain is performed to fill-in the missing pixels. In a bid to avoid flickering artifacts after frame-by-frame spatially super-resolving videos, a pixel intensity refinement stage is added to the procedure which takes into account the motion flows of the frames. Due to the use of geometric details that bandlet transform captures and the edge interpolation stage, the method results in high quality, high resolution images with no over-smoothing or blurring effect while in case of video sequences it also preserves temporal consistency. Our experimental results demonstrate the effectiveness of the proposed super-resolution technique for both still images and videos.

Explicit Ringing Removal in Image Deblurring

In this paper, we present a simple yet effective image deblurring method to produce ringing-free deblurred images. Our work is inspired by the observation that large-scale deblurring ringing artifacts are measurable through a multi-resolution pyramid of low-pass filtering of the blurred-deblurred image pair. We propose to model such a quantification as a convex cost function and minimize it directly in the deblurring process in order to reduce ringing regardless of its cause. An efficient primal-dual algorithm is proposed as a solution to this optimization problem. Since the regularization is more biased toward ringing patterns, the details of the reconstructed image are prevented from over-smoothing. An inevitable source of ringing is sensor saturation which can be detected costlessly contrary to most other sources of ringing. However, dealing with the saturation effect in deblurring introduces a non-linear operator in optimization problem. In this paper, we also introduce a linear approximation as a solution to handling saturation in the proposed deblurring method. As a result of these steps, we significantly enhance the quality of the deblurred images. Experimental results and quantitative evaluations demonstrate that the proposed method performs favorably against state-of-the-art image deblurring methods.