With the advancement of image editing tools in today's world, the manipulation of images like cropping, cloning, resizing, etc., becomes an easy proposition and on the other end, checking or determining whether an image has been manipulated or not, becomes a great challenge. Copy-move forgery in images is the most popular tampering method in which a portion of an image is copied and pasted in some other location of the same image. The detection of copy-move forgery has become a prominent research area. This study presents a detailed review and critical discussions with pros and cons of each of copy-move forgery detection techniques from 2007 to 2017. This study also addresses the variation in databases, issues, challenges, future directions and references in this domain.

This paper automatizes the process of bone age maturity assessment by applying three versions of particle swarm optimization (PSO) along with image processing methods to the left hand X-ray images. PSO versions were adopted to enhance the segmentation accuracy. The proposed method was compared to the conventional visual inspection method in terms of three segmentation criteria, classification accuracy, robustness against noise and computational complexity. Herein, PSO, worst behavior-based PSO (WB-PSO) and adaptive inertia weight (AIW-PSO) along with Otsu and an iteratively statistical method were implemented to segment the hand radiographs. A dataset containing left hand-wrist radiographs from 65 referred children was collected. Their results provided 82.49, 83.08, 84.27, 81.76 and 69.04% classification accuracy using the PSO, WB-PSO, AIW-PSO, Otsu and the iteratively statistical methods, respectively. To assess the robustness of the implemented methods, white Gaussian noise with different intensities was added to the images and the results indicated that as the noise level increased the robustness against noise for the PSO variants became more highlighted compared to the Otsu and statistical methods. Due to the convincing results, the AIW-PSO image segmentation system is suggested as an auxiliary diagnostic tool to help specialists for more accurate age bone estimation.

Sobriety test, which commonly uses a breathalyser for estimating blood alcohol content (BAC) from a breath sample, is commonly used to detect drunk driving. The detection often gives rise to sanitary concern and violation of human right. This research proposes a new method that employs non-invasive measurement of photoplethysmography (PPG) signal for detecting BAC of the test subject under the sobriety test. The PPG signal is measured via an LED transmitter and a receiver that illuminates finger and measures the changes in lighting. Since a PPG signal contains information of systolic and diastolic blood pressure, it is possible to be used for the purpose of detecting BAC. The authors have developed a practical alcohol sobriety test system to analyse the status of alcohol intake of the subject. Extensive tests have been conducted to examine feasibility of the proposed system with an identification rate up to 85%.

Side scan sonar (SSS) image matching plays an important role in underwater applications, such as combining images to form wide range relief images, underwater simultaneous location and mapping, and construction of images of underwater terrain and objects. Because there are differences between the imaging mechanisms of SSS images and optical images, current matching algorithms for optical images do not always work well for SSS images. This study proposes a combined image matching approach for SSS images. First, the images are preprocessed to remove some environmental effects. Second, feature points that are stable for affine transformations are extracted from the SSS matching images based on speeded-up robust features with prior position knowledge. Third, geometric correction is made by the random sample consensus. After that, a similarity calculation is performed. The proposed approach speeds up the matching process and improves its accuracy by combining feature points matching and similarity calculation. It reduces the mismatching rate and the computation requirement by estimating the location uncertainty with prior knowledge and reducing the searching regions for image matching. Experiments show that the matching algorithm takes less time and has greater reliability and accuracy than traditional algorithms.

License plate recognition (LPR) system plays a vital role in security applications which include road traffic monitoring, street activity monitoring, identification of potential threats, and so on. Numerous methods were adopted for LPR but still, there is enough space for a single standard approach which can be able to deal with all sorts of problems such as light variations, occlusion, and multi-views. The proposed approach is an effort to deal under such conditions by incorporating multiple features extraction and fusion. The proposed architecture is comprised of four primary steps: (i) selection of luminance channel from CIE-Lab colour space, (ii) binary segmentation of selected channel followed by image refinement, (iii) a fusion of Histogram of oriented gradients (HOG) and geometric features followed by a selection of appropriate features using a novel entropy-based method, and (iv) features classification with support vector machine (SVM). To authenticate the results of proposed approach, different performance measures are considered. The selected measures are False positive rate (FPR), False negative rate (FNR), and accuracy which is achieved maximum up to 99.5%. Simulation results reveal that the proposed method performs exceptionally better compared with existing works.

Distributed compressive video sensing (DCVS) is a novel video coding technique that shifts sophisticated motion estimation and compensation from the encoder to the decoder and is suitable for resource-limited communication, namely wireless video sensor networks (WVSNs). In DCVS, key frames serve as the reference for subsequent non-key frames in a given group of pictures (GOP). However, in fast-motion sequences, e.g. scene-changing sequences, a fixed GOP size can cause inaccuracy in the selection of reference key frames. The difference in the peak signal-to-noise ratio between key frames and non-key frames caused by this inaccuracy appears as flicker in the decoded video, negatively affecting the quality of experience. To address this problem, the authors present a perceptual hash algorithm-based adaptive GOP selection algorithm for DCVS and a novel allocation model for the frame sampling rate. In addition, the authors define several indexes to assess the degree of flicker in decoded video. The experimental results demonstrate that the proposed algorithm reduces the degree of flicker in fast-motion sequences by 40–60% relative to the state-of-the-art architecture, while also outperforming other adaptive GOP selection strategies.

Compound images are containing palletise regions including text or graphics and continuous tone images. The compression of compound images is a challenging function and which is complicated to achieve it without degrading the quality of the images. This document is mainly used to improve the compression ratio and an efficient segmentation method is created to separate the background image, text and graphics from the compound images for to make the compression independently. The segmentation is performed through AC coefficient-based segmentation method resulting in smooth and non-smooth regions. The non-smooth region is again segmented by means of K-means cuckoo optimisation algorithm. In the second phase, the segmented background image, text and graphics were compressed by means of arithmetic coder, Huffman coder and JPEG coder, respectively. This proposed technique is implemented in the working platform of MATLAB and the results were analysed.

Edge-preserving image smoothing aims at maintaining the fundamental constituents, i.e. salient edges of a given image, while removing the noise and insignificant details in the meantime. It is often employed at the pre-processing step of many image processing tasks, including reconstruction, segmentation, recognition and three-dimensional content generation, to name just a few. Recently, a sparse gradient counting scheme in an optimisation framework has attracted much attention, and it confines the discrete number of intensity changes among neighbouring pixels. This -regularised sparsity pursuit scheme performs favourably in a global optimisation manner. However, it often achieves unsatisfactory performance at diminishing trivial details and at smoothing discrete regions. In this study, a new image smoothing scheme with truncated regularisation is proposed, which is especially effective for sharpening critical edges. For objective evaluation of the smoothing performance, images are linearly quantised into several layers to generate the experimental images, then these quantised images are smoothed using several methods for reconstructing the smoothly changed shape and intensity of the original images. Compared with the smoothing scheme, extensive experimental results demonstrate that the proposed method performs much better at preserving main structures and removing trivial details.

Among the existing image coding methods, set partition in hierarchy tree (SPIHT) becomes a favourable choice for the energy-constrained embedded computing system because of its simplicity and high coding efficiency. In this study, the authors presented a quad-tree search model which is able to provide the higher coding efficiency than the model used in SPIHT. By applying this model, the authors proposed a new image codec, which is able to surpass SPIHT by 0.2–0.5 dB in peak signal-to-noise ratio over various code rates, and its performance is even comparable to SPIHT with adaptive arithmetic code and JPEG2000. Also an experiment is conducted to show the complexity of the proposed codec is the same as SPIHT without the complicated arithmetic codes. This property is critically favoured in embedded processing communication systems where energy consumption and speed are priority concerns.

The new High-Efficiency Video Coding (HEVC) standard doubles the video compression ratio compared to the previous H.264/AVC at the same video quality and without any degradation. However, this important performance is achieved by increasing the encoder computational complexity. That's why HEVC complexity is a crucial subject. The most time consuming and the most intensive computing part of HEVC is the motion estimation based principally on the sum of absolute differences (SAD) or the sum of square differences (SSD) algorithms. For these reasons, the authors proposed an implementation of these algorithms on a low cost NVIDIA GPU (graphics processing unit) using the Fermi architecture developed with Compute Unified Device Architecture language. The proposed algorithm is based on the parallel-difference and the parallel-reduction process. The investigational results show a significant speed-up in terms of execution time for most 64 × 64 pixel blocks. In fact, the proposed parallel algorithm permits a significant reduction in the execution time that reaches up to 56.17 and 30.4%, compared to the CPU, for SAD and SSD algorithms, respectively. This improvement proves that parallelising the algorithm with the new proposed reduction process for the Fermi-GPU generation leads to better results. These findings are based on a static study that determines the PU percentage utilisation for each dimension in the HEVC. This study shows that the larger PUs are the most utilised in temporal levels 3 and 4, which attain 84.56% for class E. This improvement is accompanied by an average peak signal-to-noise ratio loss of 0.095 dB and a decrease of 0.64% in terms of BitRate.

Non-local self-similar patch-based denoising techniques have been viewed as the most popular denoising approaches in computer vision. This study has proposed a novel iterative weighted sparse representation (IWSR) scheme for X-ray cardiovascular angiogram image denoising. The main procedures of this scheme include four parts. First, a maximum a posterior (MAP) distribution by the Bayes’ theory is adopted to simultaneously estimate the estimated image and sparse representation with different Gaussian distributions approximating to likelihood prior, non-local self-similar patch prior and sparse representation prior. Second, the MAP problem is converted to minimise an energy function using the logarithmic transformation. Third, the function is efficiently solved by the single and effective alternating directions method of multipliers algorithm along with singular value decomposition (SVD) algorithm. Finally, owing to learned dictionary by K-SVD algorithm, the qualitative and quantitative results of widely synthetic experiments demonstrate that the proposed IWSR denoising method performs effectively and can obtain competitive denoising performance and high-quality images compared with those advanced denoising methods. The results of extensive experiments on clinical X-ray angiogram images further illustrate that the IWSR method performs well on noise reduction and vascular structures including edges and capillaries preservation, integral cardiovascular trees of which are beneficial for clinicians to diagnose and analyse cardiovascular diseases.

Image decomposition is an important issue in image processing. The existing approaches including the bi-dimensional empirical mode decomposition (BEMD) still fail to separate monocomponents in multicomponents in many cases. To solve this problem, this study proposes a new image decomposition method based on the new derived combined bi-dimensional Bedrosian's principle that has not been reported anywhere else for image processing. First, this study investigates a few bi-dimensional Bedrosian's principles according to the bi-dimensional Hilbert transforms. Second, based on the derived bi-dimensional Bedrosian's principles and the original multicomponents, the authors provide the combined bi-dimensional Bedrosian's principle and the assisted components obtained through projections via optimisation so that these monocomponents in multicomponents can be separated in the case that the existing methods fail. Third, an iterative image decomposition method is proposed via the above principles to decompose the multicomponent image into true monocomponents. The proposed method can solve the problems caused by the cross-angle and amplitude ratio and frequency ratio between these components that BEMD fails to solve. Also, the phase and amplitude are estimated for texture analysis after the decomposition is demonstrated. Experiments are shown to support the proposed methods.

Graphical processing unit (GPU) has proven a beneficial tool in handling computationally intensive algorithms, by introducing massive parallelism in the calculations. In this study, an effective and low-cost fingerprint identification (FI) solution is proposed that can exploit the parallel computational power of GPU proficiently. It is achieved by mapping a generalised minutia neighbour-based novel encoding and matching algorithm on low-cost GPU technology. The proposed solution achieves high accuracy in comparison with two open source matchers and it is shown to be scalable by comparing matching performance on different GPUs. The proposed GPU implementation employs multithreading and loop unrolling, which minimises the use of nested loops and avoids sequential matching of encoded minutia features. After a thorough and careful designing of data structures, memory transfers and computations, a GPU-based fingerprint matching system is developed. It achieves on average 50,196 fingerprint matches per second on a single GPU. As compared to the sequential central processing unit implementation, the proposed system achieves a speed up of around 92 times, while maintaining the accuracy. The proposed system with matcher integrated on GPU can be considered as a good, low-cost, robust and efficient solution for large-scale applications of automated FI systems.

Segmentation of texture images is always a challenging problem in image processing. The authors propose a novel model for segmentation of texture images based on L ₀ gradient norm. The model will do smoothing of texture in image and segmentation jointly. It is well known that L ₀ gradient norm smooths the image and preserve the edges. Keeping this in view, the proposed model is using L ₀ gradient norm for smoothing of texture in image and Chan–Vese energy for segmentation. For fast and efficient solution of the model, the authors use alternating minimisation algorithm. Experimental results of their proposed model, which are compared with well-known (state of the art) existing models, validate better performance of the proposed model.

In this study, a novel underwater colour image enhancement approach based on hue preserving is presented by combining hue–saturation–intensity (HSI) and HS–value (HSV) colour models. In this study, the proposed wavelet-domain filtering (WDF) and constrained histogram stretching (CHS) algorithms are operated on HSI and HSV colour models, respectively. The degraded image is first converted from red–green–blue colour model into the HSI colour model, wherein the hue component H is preserved and WDF algorithm is executed on the S and I components. Similarly, the image is further converted into the HSV colour model, wherein H component is kept invariant as well and CHS algorithm is applied on the S and V components. The authors' key contribution is that the H preserving method can improve image quality in terms of contrast, colour rendition, non-uniform illumination, and denoising. In addition, experimental results show that the proposed approach outperforms several other state-of-the-art algorithms.