Image fusion has emerged as a major area of research in the past few decades due to its extended applications. While progressing in the field of image fusion, a large number of techniques-based image transforms and spatial filters have been devised for both general and specific sets of images. The primary criterion of image fusion technique is to deliver high-quality visual perception besides giving

Positron emission tomography (PET) image reconstruction from low-count projection data and physical effects is challenging because the inverse problem is ill-posed and the resultant image is usually noisy. Recently, generative adversarial networks (GANs) have also shown their superior performance in many computer vision tasks and attracted growing interests in medical imaging. In this work, the authors

Currently, the discriminative correlation filter-based trackers have achieved higher tracking accuracy. However, visual tracking still faces challenges in terms of heavy occlusion, scale variation and so on. In this study, the authors intend to solve heavy occlusion by introducing collaborative model into classifier-box. Firstly, they introduce complex colour features into correlation filter tracker

Fingerprints are widely used for biometric recognition. However, many spoofing attacks based on an artificially made fingerprint occur. In this study, the authors propose an approach to detect fingerprint liveness which uses the guided filtering and hybrid image analysis. This study deals with the problem of ignoring the contribution that is brought by the sharp features when analysing the denoised

In the most data hiding methods, the cover media is entirely distorted and it cannot be recovered when the secretive data is extracted. However, in some applications, such as medical imaging, military imaging and law enforcement, even a slight modification in the cover image is impermissible. Therefore, reversible data hiding schemes have proposed as a solution of this issue, recently. Histogram based

Red Blood Corpuscles called Erythrocytes are the most important element in blood composition which is mainly responsible in all living cells. To detect the cancer cell various methods are employed. In this paper, proper identification of cancer cells from unaffected RBCs are detected. The proposed novel method called Online Region Based Segmentation (ORBS) method is done that is used to find the regions

The fractal image compression (FIC) algorithm is difficult to be widely used in real-time applications due to the huge consumption of its encoding time. Inspired by the fact that in the decoding process, for any original image, a fixed point is generated by the iterations of the fractal codes, the authors propose a specific update search FIC (SUSFIC) algorithm, which uses a scale number to control

Bilinear matrix regression based on matrix data can directly select the features from matrix data by deploying several couples of left and right regression matrices. However, the existing matrix regression methods do not consider the local geometric structure of the samples, which results in poor classification performance. This study proposes a robust graph regularised sparse matrix regression method

Image compression is an intensively studied subject in computer vision. The deep generative model, especially generative adversarial networks (GANs), is a popular new direction for this subject. In this study, the authors propose a new compression method based on a generative model and focus on its application by GANs. The decoder in the proposed method is modified from the GAN generator model, which

Utilising different representations of face images is known to be helpful in face recognition. In this study, the authors propose two fusion techniques that make use of multiple face image features by collaboratively training a similarity metric learner, based on Siamese neural networks. This training procedure takes two (or possibly more) features of two face images and outputs a similarity score

The bag-of-visual words (BOVWs) have been recognised as an effective mean of representing images for image classification. However, its reliance on a visual codebook developed using handcrafted image feature extraction algorithms and vector quantisation via k -means clustering often results in significant computational overhead, and poor classification accuracies. Therefore, this study presents an

The image denoising model based on non-local self-similarity prior (NSS) has received extensive attention in recent years because of the repeated structure of natural image patches. Similar patches collected by exploiting NSS prior are sparse, which can be used to estimate potential lowrank subspace. Meanwhile, the modelling of natural images, such as Gaussian mixture models (GMMs), has been successful

Emulating human behaviours in automated image quality assessment (IQA) enables a comparator framework to remove the differences in human bias naturally. Based on the observation of the practical applications of IQA, this study focuses on similar-content image quality comparison based on a new image quality comparator (IQC). Outstanding proven IQAs can be utilised in this comparator to achieve a new

Segmentation of handwritten words into isolated characters and their recognition are challenging due to the presence of high variability and cursiveness in Indian scripts. The complex shapes and availability of numerous atomic character classes, compound characters, modifiers, ascendants, and descendants make the recognition task even more difficult. A holistic approach effectively tackles such issues

Histopathology image segmentation is an important area in the field of computer aided diagnosis using image processing. The segmentation of Multiple sclerosis (MS) lesions from MR images can establish the basis for subsequent lesion reconstruction, volume estimation, and course evaluation. This study proposes a method for automatically segmenting MS lesions based on 3D convolutional neural network

Remote sensing is an escalating field that helps to monitor the earth in different perspectives like vegetation assessment, coastal studies, global warming analysis etc. Presently many satellites are orbiting the earth for taking multispectral imagery, which is working behind the principle remote sensing applications. Though there are mechanisms for image classification still innovative method is required

Loopy belief propagation (LBP) suffers from high computational time, specifically when each node in the Markov random field (MRF) model has lots of labels. In this study, a swift distance transformed belief propagation (SDT-BP) method is proposed. SDT-BP employs an efficient dynamic label pruning approach together with distance transformation to boost the running time of the LBP. The proposed dynamic

Natural scene text usually carries considerable abstract semantic information, which is closely related to the surrounding environment. Thus, natural scene text detection plays a vital role in image content retrieval and understanding. In this study, the authors propose a novel specific category region proposal network (SCRPN) based on maximally stable extremal regions (MSER) and fully convolutional

The bilateral filter is a classical technique for edge-preserving smoothing. It has been widely used as an effective image denoising approach to remove Gaussian noise. The performance of bilateral filtering highly depends on the accuracy of its range distance estimation, which is used for pixel-neighbourhood similarity measurement. However, in the conventional bilateral filtering approach, estimating

Low dynamic range (LDR) image may contain low-light and highlight areas due to the limitations of the dynamic range of conventional image sensors. Low-light and highlight phenomena limit colour richness and visibility of objects in an image. Therefore, it can cause a reduction in the quality of images and a loss in accuracy in the application of image recognition. To overcome this, high dynamic range

The human eye movement tracking is possible with the assistance of the electrooculography (EOG) signals. The human eye tracking system allows researchers to analyse the participant's eye movements during certain activities. This study offers the EOG signals to control the human–computer interface systems with the help of Empirical Mean Curve Decomposition (EMCD) decomposition model. At first, the input

In this study, an improved quantum-behaved particle swarm optimisation based pulse-coupled neural network (IQPSO-PCNN) is proposed in the non-subsampled shearlet transform (NSST) domain for medical image fusion. First, NSST tool is used to decompose the source image into low-frequency and high-frequency subbands. Then, for low-frequency subbands, the fusion rules of two different functions are presented

Detection of abnormal events in the traffic scene is very challenging and is a significant problem in video surveillance. The authors proposed a novel scheme called super orientation optical flow (SOOF)-based clustering for identifying the abnormal activities. The key idea behind the proposed SOOF features is to efficiently reproduce the motion information of a moving vehicle with respect to superorientation

Compared with the predecessor H.264/advanced video coding, high-efficiency video coding (HEVC) is a new video coding standard with nearly double coding efficiency under the same coding quality. However, the computing complexity of HEVC increases sharply. To solve this problem, a fast algorithm for intra prediction mode selection based on mode grouping is proposed by reducing the number of modes entering

Ultrasound (US) imaging is the leading diagnostic method for assessing the early-stage thyroid nodule. However, the visual evaluation of nodules can be influenced by the subjectivity of radiologists' interpretations. Computer-aided Diagnostic (CAD) systems can be useful in classifying these nodules according to their benign or malignant nature. The extraction of the characteristics, which relate in

JPEG is one of the popular and efficient compression algorithms supported in the consumer electronics world. Excessive usage of mobile phones and e-governance applications have all resulted in a huge collection of JPEG compressed document images. The major challenge with these images is that its processing becomes expensive as it requires repeated decompression and recompression operations. Recently

Analysis of the chromosome images plays an important role in discovering one's genetic information and possible genetic disorders. Segmentation has a very substantial place in the chromosome analysis and without an automatic solution, it is a time-consuming and error-prone procedure. Many researchers tried to automate the segmentation process. However, background noise, objects other than chromosomes

Bias field correction is an essential pre-processing requirement for brain tissue segmentation task. Authentic brain tissue regions are highly useful for classification and detection of abnormalities. A poor resolution magnetic resonance (MR) image is produced with irregularities in structure, abnormalities in the intensity distribution and noise during the acquisition procedure. The existing bias

Infrared small target detection in a complex background has always been a challenging task in an infrared detection system. The existing methods based on the infrared patch image (IPI) model have achieved a good result but are sensitive to the complex background. So, to effectively detect the small target in complex background, model based on the reweighted IPI model along with total variance (TV)

There is intense pressure on agricultural productivity due to the ever-growing population. Several diseases affect crop yield and thus, effective control of these can significantly improve the production of food for all. In this regard, detection of diseases at an early stage and quantification of the severity, in general, has acquired urgent attention of the researchers. In this study, a summary of

Visual Sentiment Analysis aims to understand how images affect people, in terms of evoked emotions. Although this field is rather new, a broad range of techniques have been developed for various data sources and problems, resulting in a large body of research. This paper reviews pertinent publications and tries to present an exhaustive overview of the field. After a description of the task and the

Principal component analysis (PCA) is the most widely used unsupervised dimensionality reduction approach. A number of variants of PCA have been proposed to improve the robustness of the algorithm. However, the existing methods either cannot select the useful features consistently or is still sensitive to outliers. In order to reveal the intrinsic manifold structure and preserve the global structure

In this study, the authors propose a novel network architecture to address the problem of removing rain streaks from single images. To strengthen the representational power of the network, they adopt the squeeze-and-excitation block in the network. Furthermore, they propose a new network connection called reusing original input (ROI). The ROI connection reuses the original input of the network and

Low-rank representation (LRR) and its variants have been proved to be powerful tools for handling subspace clustering problems. Most of these methods involve a sub-problem of computing the singular value decomposition of an matrix, which leads to a computation complexity of . Obviously, when n is large, it will be time consuming. To address this problem, the authors introduce a fast solution, which

Pulmonary nodules risk classification in adenocarcinoma is essential for early detection of lung cancer and clinical treatment decision. Improving the level of early diagnosis and the identification of small lung adenocarcinoma has been always an important topic for imaging studies. In this study, the authors propose a deep convolutional neural network (CNN) with scale-transfer module (STM) and incorporate

In this study, the authors present a new approach to segment and classify moving objects in video sequences by combining an unsupervised anomaly discovery framework called DeepSphere and generative adversarial networks. The proposed deep detector classifier employs and validates DeepSphere, which aims mainly to identify the anomalous cases in the spatial and temporal context in order to perform foreground

Object detection is a crucial topic in computer vision. Mask Region-Convolution Neural Network (R-CNN) based methods, wherein a large intersection over union (IoU) threshold is chosen for high quality samples, have often been employed for object detection. However, the detection performance of such methods deteriorates when samples are reduced. To address this, the authors propose an improved Mask

This study proposes a strategy to refine optical flow based on the estimated reliability maps. These maps are firstly estimated a posteriori after the motion estimation by the well-known Kanade–Lucas–Tomasi (KLT). With two new defined criteria based, respectively, on the optical flow local variance and the temporal evolution of the KLT residuals, a global refinement of the motion map is then carried

Nowadays, the proficiency of Computer-Aided Diagnosis systems for early diagnosis of malignant nodules in baseline Computed Tomography (CT) scan of chest crucially depends on the authenticity of the segmented nodule. In this study, the authors introduce a new morphological feature called solidity radius (SR). They then employ this feature in the new segmentation framework for the automatic segmentation

Periodic noise corrupts digital images during acquisition and transmission stages by adding repetitive patterns. This study introduces a new adaptive Gaussian notch filter (AGNF) in Fourier transform domain for effectively restoring images contaminated with periodic, quasi-periodic and Moiré pattern noises. Since periodic noises are sinusoidal functions added to the uncorrupted images, Fourier transform

Learning-based image interpolation methods have been proved to be effective in image interpolation. In this study, the authors propose an accurate image interpolation with adaptive k -nearest neighbour searching and non-linear regression. The proposed method aims to find k -nearest neighbours of the input image patch and use them to learn the non-linear mapping between low-resolution and high-resolution

The authors address the problem of editing signals such as 2D colour images or 3D coloured meshes that are represented under the general framework of graph signals. As state-of-the-art editing approaches decompose an image into several layers in order to manipulate them, they propose a hierarchical multi-layer decomposition of graph signals that relies on morphological filtering. Since morphological

In spite of the recent advancements of deep learning based techniques, automatic photo aesthetic assessment still remains a challenging computer vision task. Existing approaches used to focus on providing a single aesthetic score or category (“good” or “bad”) of photograph, rather than quantifying “goodness” or “badness”. The existing algorithms often ignore the importance of different attributes contributing

Age-related Macular Degeneration (AMD) and Diabetic Macular Edema (DME) are the major causes of vision loss in developed countries. Alteration of retinal layer structure and appearance of exudates are the most significant signs of these diseases. In this paper, with the aim of automatic classification of DME, AMD, and normal subjects using Optical Coherence Tomography (OCT) images, a dictionary-learning

Automatic detection of pavement crack is an important task for conducting road maintenance. However, as an important part of the intelligent transportation system, automatic pavement crack detection is challenging due to the poor continuity of cracks, the different width of cracks, and the low contrast between cracks and the surrounding pavement. This study proposes a novel pavement crack detection

In this study, an efficient self-learning method for image super-resolution (SR) is presented. In the proposed algorithm, the input image is divided into equal size patches. Using these patches, a dictionary is learned based on K-SVD, referred to as high resolution (HR) dictionary. Then, by down-sampling, the columns of the dictionary, called atoms, a low resolution (LR) version of the dictionary is

The addition of new words in handwritten documents such as bank cheques, bills, and notes is considered as common crime. Such immoral activities on handwritten documents have a bad effect on the victim in terms of mental and financial loss. For facilitating an impartial judicial process, it is important to differentiate between the used pen inks. Earlier, image processing and pattern recognition-based

Computer screen images such as wallpaper, web pages and powerpoint images are compound images. As these images contain a mixture of textual, graphical, pictorial and smooth regions, compression of computer screen images necessitates accurate classification and segmentation of these regions. In this study, an improved compound image segmentation using automatic block classification with support vector

Image segmentation is a process of partitioning an image into non-overlapping regions. Existing unsupervised image segmentation methods include level set, automatic thresholding and region-based CV mode and so on. However, image segmentation as a key technology in the field of image processing has not been solved indeed, especially for images with complex texture. For this reason, the authors proposed

The challenging problem that the authors solved in this study is to precisely estimate the number of objects in an image. Combining the spatial attention mechanism and pyramid structure, a novel atrous pyramid attention module is introduced to extract precise dense multi-scale features for object counting. Also, a global attention feature module is designed to enhance the ability of the network to

This study presents a technique to automatically extract the aircraft from video sequences. The technique incorporates object tracking algorithm to separate the region of interest (ROI), i.e. movement of aircraft, from video sequence, to reduce the computational complexity. Trimaps are generated automatically for given ROI using non-rigid image registration to obtain the prior information of the object

Visual object tracking remains a challenging task in computer vision although important progress has been made in the past decades. Particle filter (PF) is now a standard framework for solving non-linear/non-Gaussian problems, especially in visual object tracking. This study proposes an ant colony optimisation (ACO)-based iterative PF for object tracking. In the proposed method, the basic idea of ACO

Similar to the basic facial expression recognition, one challenge for pain intensity recognition is some individual characteristics, e.g. face shapes, may cause great diversities in the same emotion. So it is usually very difficult to distinguish two adjacent intensity levels of pain expression as each intensity has a large variation. In this study, a coarse-to-fine combination method is proposed for

Deep learning-based approaches have become very prominent in recent years due to its outstanding performance as compared to the hand-extracted feature-based methods. Convolutional neural network (CNN) is a type of deep learning architecture to deal with the image/video data. Residual network and squeeze and excitation network (SENet) are among recent developments in CNN for image classification. However

Convolution neural network (CNN) is an efficient technique to detect objects in various kinds of images, especially for microaneurysm (MA) of diabetic retinopathy in retinal fundus image. This study proposes a deconvolutional neural network to accurately discriminate MA from non-MA. The deconvolution, instead of pooling operation, is embedded into the CNN to recover the erased details of feature maps

Salient object detection has been revolutionised by convolutional neural network (CNN) recently. However, it is hard to transfer the state-of-the-art still-image based saliency detectors to videos directly, owing to the neglect of temporal contexts between frames. In this study, the authors propose a flow-driven attention network (FDAN) to exploit motion information for video salient object detection

The crucial hurdle faced by the watermarking technique is to maintain the steadiness corresponding to several attacks while assisting a sufficient level of security. In this study, a robust lossless sparse domain-based watermarking approach combined with discrete cosine transform (DCT) is introduced to hide the secret message in the selected significant sparse elements of the host image. The proposed

In recent years, many image encryption approaches have been proposed on the basis of chaotic maps. The various types of chaotic maps such as one-dimensional and multi-dimensional have been used to generate the secret keys. Chaotic maps require some parameters and value assignment to these parameters is very crucial. Because, poor value assignments may make the chaotic map un-chaotic. Therefore, hyper-parameter

During the process of watching 3D synthesised video (3D-SV) and switching viewpoints, there is a case of asymmetric distortion, the left(right) viewpoint is a synthesised video generated by rendering technique, and the right(left) viewpoint is a real video taken by the camera. How to accurately estimate the quality of 3D-SV with binocular asymmetric distortions is a new and challenging problem. Aiming

Classical morphological filters suffer from well performing in a noisy environment, and intrinsic image structures are not taken into account. The authors propose here an alternative to overcome such weaknesses, by properly using robust shock filters and inhomogeneity. Thus, they obtain multiscale morphological operators by using image edge functions as local weights in inhomogeneous Hamiltonians in