In this study, a novel adaptive sparse representation (ASR) model is presented for simultaneous image fusion and denoising. As a powerful signal modelling technique, sparse representation (SR) has been successfully employed in many image processing applications such as denoising and fusion. In traditional SR-based applications, a highly redundant dictionary is always needed to satisfy signal reconstruction requirement since the structures vary significantly across different image patches. However, it may result in potential visual artefacts as well as high computational cost. In the proposed ASR model, instead of learning a single redundant dictionary, a set of more compact sub-dictionaries are learned from numerous high-quality image patches which have been pre-classified into several corresponding categories based on their gradient information. At the fusion and denoising processes, one of the sub-dictionaries is adaptively selected for a given set of source image patches. Experimental results on multi-focus and multi-modal image sets demonstrate that the ASR-based fusion method can outperform the conventional SR-based method in terms of both visual quality and objective assessment.

An objective novel evaluation approach, implemented by the benchmark function-based peak signal-to-noise ratio, particularly suitable for evaluating the performance of a large-scale enlargement of a small size image is proposed in this study. Also, a fast large-scale image enlargement method via the improved discrete cosine transform (DCT) is proposed to improve the quality and speed of image zooming. The proposed image enlargement algorithm based on DCT saves computation time by multiplication of the DCT matrix. Compared with the traditional DCT approach, the improved approach overcomes the image shifting and blocky effects. In comparisons with other interpolation methods, DCT enlargement outperforms them in edge details because it considers the global frequency information of the whole image. With the DCT enlargement, it is easy to implement the arbitrary pixel-size-based zooming of an image by employing the different size of transform matrix. Illustrative examples show the effectiveness of the proposed approach.

In colour compressed sensing (CS) imaging, the current two bottlenecks for application are (1) high computation cost of sparse representation (SR) with over-complete dictionary and (2) unsatisfactory imaging quality of CS recovery with l ₁-norm minimisation. Thus, this study proposes a novel colour CS imaging framework. In the framework, two improvements are achieved: (1) the authors present the sparse difference to reduce the computation cost of SR in RGB colour imaging; (2) the authors use fractal dimension instead of l ₁-norm as the object function to actualise high quality CS recovery. The feasibility of our colour CS imaging framework is proved by sseveral experiments.

In this study, the authors present a new system for sign language hand gesture recognition. Using video input, the system can recognise any spelled word or alphabetic sequence signed in American Sign Language. The three main steps in the recognition process include detection of the region of interest (the hands), detection of key frames and recognition of gestures from these key frames. The proposed segmentation algorithm distinguishes regions of interest from both uniform and non-uniform backgrounds with an efficiency of 95%. The proposed key frame detection algorithm achieves an efficiency of 96.50%. A rotation-invariant algorithm for feature extraction is additionally proposed, which provides an overall gesture recognition efficiency of 84.2%.

Image segmentation plays a crucial role in object-based remote sensing information extraction. This study improves the existing mean shift (MS) algorithm for segmenting high resolution remote sensing imagery by adopting two strategies. First, a pixel-based, fixed bandwidth and weighted MS algorithm is applied to cluster the image. In this process, the space bandwidth is selected according to the resolution of remote sensing images, and the range bandwidths of each band are calculated based on grey feature and the plug-in rule. Gaussian kernels are used for clustering. Second, a region-based MS algorithm is applied to globally merge modes which are obtained in the first step. The spatial and range bandwidths are adaptively adjusted based on the clustering result of the first step. Experimental results with two Quickbird images show that the improved algorithm is superior to the typical MS algorithm, producing high precision and requiring less operation time.

Impulsive noise is one of the major challenges for reliable transmission over power lines. Interleavers provide higher protection against the impulsive noise by dispersing information across the channel and spreading the burst of errors over multiple codewords. Multi-fold turbo (MFT) coding is a technique that improves the communication reliability using multiple interleavers. In the MFT codes, each data subsequence is equally protected. For applications in which data constitute information with various levels of importance, it is intuitive to offer the more important subsequence, a stronger protection. A modified form of the MFT codes capable of providing unequal error protection over a two-user power-line binary adder channel is proposed here. As a benchmark, two test images are transmitted across the channel. The trellis-based iterative algorithm is modified for the two-user scenario to decode the received signal. The simulation results show a gain of 1.5 dB for the modified MFT code over the conventional turbo codes for each of the transmitted images. A gain of 2 dB is also recorded for the most protected component of each image over the least protected components.

Biometric information is widely used in user identification systems. Iris is one of the most reliable and accurate biometric information. In an iris recognition system, the iris localisation is one the most important parts because the performance of an iris recognition system is highly dependent on the accuracy of iris localisation. If unreliable iris regions are used in the iris recognition system, the recognition rate may be degraded. Therefore many researchers have studied the iris localisation methods. Especially, localising an iris region from noisy images is one of the hot topics in the iris recognition researches. In this study, the authors are concentrating on the iris localisation, where an efficient iris localisation method for noisy iris images is proposed. The proposed iris localisation method consists of two steps: pupil boundary localisation and iris boundary localisation. To localise a pupil region, an efficient block-based minimum energy detection method is used, in which specular reflection removal is performed as a preprocessing. Iris boundary is localised using a guided filter, the circular Hough transform and an ellipse fitting method. Experimental results with various test image sets show the effectiveness of the proposed method.

The rapid growth in the use of multimedia information has made the security of data storage and transmission important in avoiding unlawful, unofficial, unauthorised and illegal use. Encryption is an efficient operation to protect multimedia data secret. A new image encryption approach that uses binary coded decimal (BCD) code-based decomposition, reordering and scrambling bit planes, and an encryption process is suggested in this study. Image decomposition using BCD code for image encryption is introduced in this study. A simple scrambling process is used to shuffle binary bit planes after re-ordering them. A shift column operation is applied to the image that is constructed after scrambling the bit planes to increase the security level. A performance analysis and a comparison with other encryption algorithms are conducted to prove the proposed algorithm's image encryption capabilities. The experimental results show that the suggested method protects secret images against common attacks with shorter encryption/decryption times.

Apoptotic programmed cell death (PCD) is a fundamental aspect of developmental maturation. However, the authors’ understanding of apoptosis, especially in the multi-cell regime, is incomplete because of the difficulty of identifying dying cells by conventional strategies. Real-time in vivo microscopy of Drosophila, an excellent model system for studying the PCD during development, has been used to uncover plausible collective apoptosis at the tissue level, although the dynamic regulation of the process remains to be deciphered. In this work, the authors have developed an image-analysis program that can quantitatively analyse time-lapse microscopy of live tissues undergoing apoptosis with a fluorescent nuclear marker, and subsequently extract the spatiotemporal patterns of multicellular response. The program can process a large number of cells (>10³) automatically tracked across sets of image frames. It is applied to characterise the apoptosis of Drosophila wing epithelium at eclosion. Using the natural anatomic structures as reference, the authors identify dynamic patterns in the progression of PCD within the Drosophila tissues. The results not only confirm the previously observed collective multi-cell behaviour from a quantitative perspective, but also reveal a plausible role played by the anatomic structures, such as the wing veins, in the PCD propagation across the Drosophila wing.