IET Image Processing

Glaucoma is a blindness causing eye disease if not treated in time and caused by the increase in the cup-to-disc region (CDR). A novel method for extraction of the inner limiting membrane (ILM) and retinal pigment epithelium (RPE) layers from optical coherence tomography scans is proposed. A new colour channels mean based quality assessment step is applied to segment out ILM layer for different qu... View full abstract»

Information hiding is particularly used for security applications to protect the secret message from an unauthorised person. Due to the tremendous development of the Internet and its usage, the issue of protection over the internet is increasing. Under such a condition, transforming the information from the transmitter to the receiver requires more security. Accordingly, in my previous research, a... View full abstract»

Classification is a principle technique in hyperspectral images (HSIs), where a label is assigned to each pixel based on its characteristics. However, due to lack of labelled training instances in HSIs and also its ultra-high dimensionality, deep learning approaches need a special consideration for HSI classification. As one of the first works in the HSI classification, this study proposes a novel... View full abstract»

Image denoising is still a challenging problem in image processing. The authors propose a novel image denoising method based on a deep convolution neural network (DCNN). Different from other learning-based methods, the authors design a DCNN to achieve the noise image. Thus, the latent clear image can be achieved by separating the noise image from the contaminated image. At the training stage, the ... View full abstract»

For the spatial-spectral classification of hyperspectral images (HSIs), a deep learning framework is proposed in this study, which consists of convolutional neural networks (CNNs) and Markov random fields (MRFs). Firstly, a CNN model to learn the deep spectral feature from the HSI is built and the class posterior probability distribution is estimated. The CNN with a dropout layer can relieve the o... View full abstract»

This work addresses the problem of searching and retrieving similar textual images based on the detected text and opens the new directions for textual image retrieval. For image retrieval, several methods have been proposed to extract visual features and social tags; however, to extract embedded and scene text within images and use that text as automatic keywords/tags is still a young research fie... View full abstract»

Turbid underwater environment poses great difficulties for the applications of vision technologies. One of the biggest challenges is the complicated noise distribution of the underwater images due to the serious scattering and absorption. To alleviate this problem, this work proposes a deep pixel-to-pixel networks model for underwater image enhancement by designing an encoding-decoding framework. ... View full abstract»

Low-light enhancement methods suffer from the over-enhancement problem which could induce the loss of the important texture and make images look unnatural. Moreover, some low-light images contain strong light areas that must be weakened to improve the visual effect. In this study, an enhancement method with strong light weakening and bright halo suppressing is presented. Firstly, the bright channe... View full abstract»

An improved single image fast deblurring algorithm based on hyper-Laplacian constraint is proposed. The algorithm is improved in three aspects: image blur kernel estimation sub-region selection, blur kernel precise estimation, and fast non-blind deconvolution. First, image amplitude and gradient are used as the basis of blur kernel estimation. On the basis of analysing the edge amplitude and gradi... View full abstract»

Most of the impulse denoisers are either median filter-based or fuzzy filter-based, which can only perform well in low noise conditions. This study presents an efficient convolutional neural network (CNN) with particle swarm optimisation (PSO) model for high-density impulse noise removal. The proposed high-density impulse noise detection and removal model mainly consists of two parts: the impulse ... View full abstract»

Much of medical knowledge is stored in the biomedical literature, collected in archives like PubMed Central that continue to grow rapidly. A significant part of this knowledge is contained in images with limited metadata available which makes it difficult to explore the visual knowledge in the biomedical literature. Thus, extraction of metadata from visual content is important. One important piece... View full abstract»

As a weakly supervised learning technique, multiple instance learning (MIL) has shown an advantage over supervised learning methods for automatic detection of diabetic retinopathy (DR): only the image-level annotation is needed to achieve both detection of DR images and DR lesions, making more graded and de-identified retinal images available for learning. However, the performance of existing stud... View full abstract»

Image classification by the convolutional neural network (CNN) has shown its great performances in recent years, in several areas, such as image processing and pattern recognition. However, there is still some improvement to do. The main problem in CNN is the initialisation of the number and size of the filters, which can obviously change the results. In this study, the authors assign three major ... View full abstract»

Pose detection of small targets in poor imaging conditions like heavy occlusion and low resolution is still an open and challenging task in computer vision. For instance, detection of students' poses in classrooms that are even indistinguishable to human eyes remains a rather difficult task. Motivated by the success of convolutional feature merging and locality preserving, the authors propose a po... View full abstract»

Dimensionality reduction is of high importance in hyperspectral data processing, which can effectively reduce the data redundancy and computation time for improved classification accuracy. Band selection and feature extraction methods are two widely used dimensionality reduction techniques. By integrating the advantages of the band selection and feature extraction, the authors propose a new method... View full abstract»

Segmentation of lumen (LB) and outer wall boundaries (OB) of carotid artery in magnetic resonance (MR) images is essential for carotid atherosclerotic disease diagnosis. However, the limited image signal-to-noise ratio, flow artefact, and varied lumen and outer wall become significant obstacles for automatic segmentation. A fully automatic framework is proposed for LB and OB segmentation in MR ima... View full abstract»

In this study, the author proposes a total variational (TV) driven image restoration using discrete orthogonal Stockwell transform (DOST) when the noise (in the image) is an outcome of a Poisson process. Stockwell transform or S-transform (ST) is well known for its efficiency in resolving spatio-frequency components with high accuracy compared with many other transforms such as short-term Fourier ... View full abstract»

Kernel joint sparse representation (KJSR) performs joint sparse representation in the feature space and has shown good performance for the hyperspectral image (HSI) classification. In order to distinguish spatial neighbouring pixels in the feature space, we propose two weighted KJSR (WKJSR) methods in this paper. The first one computes the weight directly based on the kernel similarity between nei... View full abstract»

In the present scenario, retinal image processing is toiling hard to get an efficient algorithm for de-noising and segmenting the blood vessel confined inside the closed curvature boundary. On this ground, this study presents a hybrid active contour model with a novel preprocessing technique to segment the retinal blood vessel in different fundus images. Contour driven black top-hat transformation... View full abstract»

This study proposes a contour-based corner detector using the magnitude responses of the imaginary part of the Gabor filters on contours. Unlike the traditional contour-based methods that detect corners by analysing the shape of the edge contours and searching for local curvature maxima points on planar curves, the proposed corner detector combines the pixels of the edge contours and their corresp... View full abstract»

In this study, a novel and efficient fog removal algorithm is proposed. Fog formation is because of attenuation and airlight. Attenuation reduces the contrast and airlight increases the whiteness in the scene. Proposed algorithm uses anisotropic diffusion to recover scene contrast. Simulation results demonstrate that proposed algorithm outperforms prior state-of-the-art algorithms in terms of cont... View full abstract»

With the rapid development of computer science, problems with digital products piracy and copyright dispute become more serious; therefore, it is an urgent task to find solutions for these problems. In this study, the authors' develop a digital watermarking algorithm based on a fractal encoding method and the discrete cosine transform (DCT). The proposed method combines fractal encoding method and... View full abstract»

Development of automatic disease detection and classification system is significantly explored in precision agriculture. In the past few decades, researchers have studied several cultures exploiting different parts of a plant. A similar study is performed for Soybean using leaf images. A rule based semi-automatic system using concepts of <i>k</i>-means is designed and implemented to di... View full abstract»

Target detection of hyperspectral image (HSI) is a research hotspot in the field of remote sensing. It is of particular importance in many domains, especially in military application. Unsupervised target detection is usually more difficult because there is no prior information about target. Traditional algorithms exploit spectral information, only. This study introduces the idea of saliency detect... View full abstract»

Two-dimensional transforms are extensively used for speckle noise reduction (despeckling) in ultrasound images. This work proposes a double filter bank structure consisting of a discrete wavelet packet transform (DWPT) and directional filter bank (DFB) along with a fuzzy-based clustering technique to despeckle ultrasound images. Wavelet packet transform can efficiently reject noises based on grey ... View full abstract»

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 featu... View full abstract»

Digital watermarking has been suggested as a way to achieve digital protection. The aim of digital watermarking is to insert the secret data into the image without significantly affecting the visual quality. This study presents a robust block-based image watermarking scheme based on the singular value decomposition (SVD) and human visual system in the discrete wavelet transform (DWT) domain. The p... View full abstract»

In certain applications, classification models have to be trained with small datasets. This study proposes a new deep neural network with a feature generalisation layer (FGL). First, instead of using a generative network for data augmentation, the FGL is modelled using a latent variable model to diversify features directly by sharing other layers. Then, dual-objective functions are defined to opti... View full abstract»

Clustering for hyperspectral images (HSIs) is a very challenging task because HSIs usually have large spectral variability, high dimensionality, and complex structures. The main issue of this study is to develop an improved sparse subspace clustering (SSC) method for HSIs. As an extension of spectral clustering, SSC algorithm has achieved great success; however, the direct self-representation dict... View full abstract»

Feature selection, which is called band selection for hyperspectral data, is widely used for hyperspectral images. A novel hyperspectral band selection method based on combined fast and adaptive tridimensional empirical mode decomposition (cFATEMD) is proposed in this study. The hyperspectral data is decomposed into a set of tridimensional intrinsic mode functions (TIMFs) and a residual (RES) by F... View full abstract»

Lung cancer is one of the deadliest diseases worldwide and the classification of different types of lung cancers in computed tomography (CT) images is also one of the most significant issues in computer-aided diagnosis. It remains a tough task since various image features could be extracted from one single image while part of the features is irrelevant to the final diagnosis results. In this study... View full abstract»

Incorporation of spatial information besides rich spectral information of hyperspectral image significantly enhances data classification accuracy. A morphology-based feature extraction and classification framework is proposed here, which includes the local neighbourhood information in a spatial window for extension of training set. The proposed method is morphology-based structure-preserving proje... View full abstract»

Band selection is an important data dimensionality reduction tool in hyperspectral images (HSIs). To identify the most informative subset band from the hundreds of highly corrected bands in HSIs, a novel hyperspectral band selection method using a crossover-based gravitational search algorithm (CGSA) is presented in this study. In this method, the discriminative capability of each band subset is e... View full abstract»

To extend the application of the cognitive associative network and improve the performance of the target tracking method, a novel tracking method based on the new network is proposed, i.e. the cognitive associative network is transformed and used to perform the target tracking. The local hue histograms are used to model the target, and the circle matching criterion is used to locate the target, wh... View full abstract»

Glaucoma is one of the leading causes of blindness in the world. Optic disc segmentation is an indispensable step for automatic detection of glaucoma with fundus images. In this study, the authors propose an automatic optic disc segmentation approach using adversarial training. The improved `U-Net' is used as the segmentation network to detect optic disc from fundus images, and then the authors ad... View full abstract»

Here, a hybrid feature descriptor-based method is proposed to recognise human emotions from their facial expressions. A combination of spatial bag of features (SBoFs) with spatial scale-invariant feature transform (SBoF-SSIFT), and SBoFs with spatial speeded up robust transform are utilised to improve the ability to recognise facial expressions. For classification of emotions, <i>K</i>... View full abstract»

Automatic extraction of retinal blood vessels plays an important role in the diagnosis of many retinal diseases and also for diagnosing several complicated diseases such as stroke, hypertension and cardiovascular diseases. Due to the complex nature of retinal vessel network, the manual segmentation of vessels is a tedious task which also requires high training and skills. This study presents a new... View full abstract»

In this study, a multiple hypothesis tracking (MHT) algorithm for multi-target multi-camera tracking (MCT) with disjoint views is proposed. The authors' method forms track-hypothesis trees, and each branch of them represents a multi-camera track of a target that may move within a camera as well as move across cameras. Furthermore, multi-target tracking within a camera is performed simultaneously w... View full abstract»

The authors propose an automatic method for extracting objects with fine quality from photographs. The authors' method starts with finding bounding boxes that enclose potential objects, which is achievable by state-of-the-art object proposal methods. To further segment objects within obtained bounding boxes, the authors propose a new multi-pass level-set method based on saliency detection and fore... View full abstract»

The authors propose a tracking algorithm based on the reliability analysis of the convolutional neural network to avoid drift. In general, most tracking algorithms implemented with the deep network consist of a single network; they obtain the tracking results according to the confidence and perform updates with the samples, which are collected based on the previous target state. However, this kind... View full abstract»

In this study, the authors introduce new solutions and improvements to the multi-channel blind image deconvolution problem. More precisely, authors' contributions are threefold: (i) At first, a simplified version of the existing cross-relation method for blind system identification is proposed; but most importantly, the authors incorporate into the channel estimation cost function a sparsity const... View full abstract»

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-... View full abstract»

Hyperspectral images are mixtures of spectra of materials in a scene. Accurate analysis of hyperspectral image requires spectral unmixing. The result of spectral unmixing is the material spectral signatures and their corresponding fractions. The materials are called endmembers. Endmember extraction equals to acquire spectral signatures of the materials. In this study, the authors propose a new hyp... View full abstract»

Chaotic mapping has been widely used in image encryption given the unpredictability, ergodicity, and sensitivity of parameters and initial values and the high correspondence with cryptography. The logistic map has the disadvantages of uneven distribution, low security, and small parameter space. In order to overcome these disadvantages, in this article, a new chaotic map based on a real-time varia... View full abstract»

Advances in the eye care telemedicine system aid the diabetic patients in remote areas to stop the unwanted visit to ophthalmologist, reduces overall cost, time and money. Diabetic retinopathy, which is the primary cause of sight loss, has the most common symptoms like microaneurysms, haemorrhages, cotton-wool spots, exudates and drusen. In this work, an efficient approach for the automatic detect... View full abstract»

In the field of computer-aided recognition, edge feature is one of the key factors to determine recognition performance. Comparing to an optical image, since sonar image via acoustic wave is easily influenced by underwater environments such as particle density, temperature, and current, edge information should be boosted. Some image preprocessing techniques based on transform domain such as wavele... View full abstract»

Inter-frame forgery marks a central type of forgery in surveillance videos, and involves three aspects - frame duplication, insertion, and deletion - under temporal domain. However, this forgery type has received little attention from scholars. More efforts have been on detecting only a single aspect of inter-frame forgery. Furthermore, studies have confirmed that previous methods did not achieve ... View full abstract»

This study presents an image denoising technique using multiscale non-local means (NLM) filtering combined with hard thresholding in curvelet domain. The inevitable ringing artefacts in the reconstructed image - due to thresholding - is further processed using a guided image filter for better preservation of local structures like edges, textures and small details. The authors decomposed the image ... View full abstract»

Recently, tooth decay detection is considered as one of the emerging topics. Many diagnostic techniques have been successfully presented to diagnose the problems. However, the complexity in the tooth decaying diagnosis ascends when the environs are moderately difficult. Thus, this study introduces a novel caries detecting model for the accurate detection of tooth cavities. The model is divided int... View full abstract»