Much research has been conducted on fuzzy c-means (FCM) clustering algorithms for image segmentation that incorporate the local neighbourhood information into their objective function in order to mitigate problems related to noise sensitivity and poor performance. Although the bias-corrected FCM, FCM with spatial constraints, and adaptive weighted averaging algorithms have proven to be robust to noise for image segmentation using local spatial image information, they have some disadvantages: (i) they are limited to single feature input data (i.e. intensity level feature), (ii) their robustness to noise and effectiveness heavily depend on a crucial parameter α, and (iii) it is difficult to find the optimal value of α, which is generally selected experimentally. In this study, to overcome all of these disadvantages, the authors present a generalisation of these types of algorithms that is applicable to cluster M-features input data. The proposed generalised FCM clustering algorithm with local information (GFCMLI) not only mitigates the disadvantages of standard FCM, but also highly improves the overall clustering performance. Experiments have been performed on several noisy data and natural/real-world images in order to demonstrate the effectiveness, efficiency, and robustness to noise of the GFCMLI algorithm as compared with conventional methods.

Sparse coding (SC) has recently become a widely used tool in signal and image processing. The sparse linear combination of elements from an appropriately chosen over-complete dictionary can represent many signal patches. SC applications have been explored in many fields such as image super resolution (SR), image-feature extraction, image reconstruction, and segmentation. In most of these applications, learning-based SC has provided an excellent image quality. SC involves two steps: dictionary construction and searching the dictionary using quadratic programming. This study focuses on the searching step and a new adaptive variation of genetic algorithm is proposed to search and find the optimum closest match in the dictionary. Also, inspired by the proposed evolutionary SC (ESC), a single-image SR algorithm is proposed. A sparse representation for each patch of the low-resolution input image is obtained by ESC and it is used to generate the high-resolution output image. Experimental results show that the proposed ESC-based method would lead to a better SR image quality.

Block-matching motion estimation (BME) can efficiently reduce the temporal redundancy between the successive video sequences in video compression coding system. In this study, a fast BME algorithm using multilevel distortion search in Walsh–Hadamard domain is proposed to reduce the computational burden and speed up coding process. First, the proposed algorithm divides the block into several sub-blocks. Then, the Walsh–Hadamard transform is applied to these sub-blocks. Finally, the proposed algorithm calculates the partial block matching distortion by utilising a novel back diagonal search scheme which can quickly reject unnecessary candidate block in a multilevel manner. Experimental results show that the proposed algorithm effectively reduces the number of operations in block distortion calculation meanwhile maintains the best motion estimation matching quality. Compared with the full search, the proposed algorithm can reduce 87.19% computational complexity without any degradation of the peak signal to noise ratio. In addition, compared with the partial distortion search algorithm, successive elimination algorithm, multilevel successive elimination algorithm and the transform-domain successive elimination algorithm, the proposed algorithm can also save 68.27, 70.09, 37.81 and 37.44% computational complexity, respectively. Moreover, the proposed algorithm can also be easily incorporated into any block-based template search motion estimation algorithm.

In this study, the authors present a new approach to detect fire flame by processing and analysing the stationary camera videos. For a fire detection system, it is desired to be sensitive and reliable. The proposed method improves not only the sensitivity but also the reliability through reducing the susceptibility to false alarms. The proposed approach based on multi-feature, i.e. chromatic features, dynamic features, texture features, and contour features, can both improve the sensitivity and reliability in fire detection. In their approach, the authors adopt a novel algorithm to extract the moving region and analyse the frequency of flickers. Experimental results show that the proposed method can run in real-time and performs favourably against the state-of-the-art methods with higher accuracy in fire videos, lower false alarm rates in non-fire videos and faster response time.

Different wavelengths of light may undergo changes in underwater environment resulting in altered images. For example, the presence of floating particles causes underwater images to appear bluish and blurred. In this study, the authors propose a method called the deep sparse non-negative matrix factorisation (DSNMF) to estimate the illumination of an underwater image. The image under observation is divided into patches and each channel of a single patch is reshaped as an [ R, G, B ] matrix. The DSNMF method deeply factorises each input matrix into multiple layers with a sparseness constraint. The last layer of the factorised matrix is used as the illumination of the patch. The sparseness constraint adjusts the appearance of the final image. After factorisation, the estimated illumination is applied to each patch of the original image to obtain the final image. Compared with state-of-the-art underwater image enhancement methods using no reference image quality assessment, not only does the proposed method outperforms current techniques in terms of its visual effect and IQA, but is also simpler to implement.

To further improve the detection rate with relatively low dimension feature vector, a novel passive splicing detection method using textural features based on the grey level co-occurrence matrices, namely TF-GLCM, is proposed in this study. In the TF-GLCM, the GLCM are calculated based on the difference block discrete cosine transform arrays to capture the textural information and the spatial relationship between image pixels sufficiently. The discriminable properties contained in the GLCM are described by six textural features, which include two new introduced ones and four independent ones. In addition, the statistical moments mean Me and standard deviation SD of textural features are used instead of themselves as elements in feature vector to reduce the dimensionality of feature vector and computational complexity. A support vector machine is employed for classification purpose. Experimental results show that the TF-GLCM achieves the detection rates of 98% on CASIA v1.0, and 97% on CASIA v2.0 with 96-D feature vector. The detection rates benefit from the two new textural features. Meanwhile, the TF-GLCM is superior to some state-of-the-art methods with lower dimension feature vector.

Various image priors, such as sparsity prior, non-local self-similarity prior and gradient histogram prior, have been widely used for noise removal, while preserving the image texture. However, the gradient histogram prior used for texture enhancement sometimes generates false textures in the smooth areas. In order to address these problems, the authors propose a robust algorithm combining gradient histogram with sparse representation to obtain good estimates of the sparse coding coefficients of the latent image and realising image denoising while preserving the texture. The proposed model is solved by having a balance between over-enhancement and over-smoothing of the texture in order to preserve the natural texture appearance. Experimental results demonstrate the efficiency and effectiveness of the proposed method.

Speckle noise is the main cause of image degradation in optical coherence tomography, which makes denoising an essential process to obtain quality images. This study proposes a wavelet-based denoising technique in which detail coefficients are assigned weights using similarity measures of Pearson's correlation coefficient and structural similarity index (SSIM). Stationary wavelet transform is used for SSIM which is an image quality measure is used as optimisation criterion to denoise images in this study. Procedure of weight computation is discussed in detail. Average of these detailed components is used to denoise the images. Comparison of proposed technique with the existing techniques has been carried out at length. Extensive qualitative and quantitative analysis reveal that the proposed technique is efficient and performs better in terms of noise reduction while maintaining the structural contents of the image.

To provide a fast compression algorithm for high-resolution medical image sequences, an efficient three-dimensional (3D) separate descendant-based (SBD) set partitioning in hierarchical trees (SPIHT) algorithm (3D SDB-SPIHT) is proposed in this study. To accelerate the transformation, 3D integer wavelet transform is used first. Based on an efficient spatial–temporal tree structure, which is designed for the transformed coefficients, the authors propose a fast coding scheme by separating the descendant set into offspring set and leaves set. The proposed algorithm has more selectivity in deciding the scanning and coding of the descendant sets and hence the coding time is accelerated. Experimental results demonstrate that 3D SDB-SPIHT compresses medical images faster compared with traditional 3D SPIHT and other variations of 3D SPIHT.