Over the past decade, much research has been done to improve single-fog images. However, most of these have concentrated on outdoor environments and little has been done for indoor environments. In this study, an effective method of removing fog from images both indoors and outdoors is presented. A new single image enhancement approach is based on mixture of dark channel prior (DCP) and contrast limited adaptive histogram equalisation with discrete wavelet transform (CLAHE-DWT) algorithms. With the DCP algorithm using modified transmission map, the authors obtained fast processing speed and clean dehazed image without refining process. The CLAHE and DWT methods improved the contrast and sharpness of images. Finally, an enhanced image was produced by fusing the CLAHE and DWT images. To demonstrate the effectiveness of the proposed method, the authors performed objective image quality assessments, and so on. Through a variety of experiments for various indoor and outdoor images with fog, the proposed method was proven to be highly effective.

A novel chaos-based colour image encryption algorithm is proposed, which adopts a one-time key mechanism based on message-digest algorithm 5 (MD5) value of the input plain image. The algorithm combines several key technologies including fractional Fourier transform (FrFT), MD5, and global scrambling. Using fast digital discrete FrFT, the algorithm develops the complex data manipulating potentials of FrFT efficiently; meanwhile, keeps the size of the cipher image un-changed, thus eliminates the requirement on double storage space to store complex values of cipher image. Exploiting the intrinsic robustness of FrFT, the algorithm achieves high robustness to pixel errors and noise attacks. Experimental results show that the algorithm achieves better pixel change rate and unified average change intensity, and is efficient, effective, and robust to attacks.

This study presents a hash rearrangement scheme to improve coding efficiency of high-efficiency video coding for screen content (HEVC-SCC) by sharing hashes of the inter-search with intra block copy (IBC). Of the various methods introduced during the HEVC-SCC development, the IBC search technique can yield tremendous coding gains, but creates a massive computational burden on the encoder side. The authors propose an effective way to generate the IBC hash table to avoid redundant operations required for hash entry computation. Moreover, the authors propose a hash rearrangement scheme to apply the second hashes used in the inter-search to IBC and the corresponding IBC search method to reduce the computational burden and to improve the coding efficiency. The experimental results show that compared with the HEVC-SCC test model (SCM)-8.0, the proposed algorithm results 80% time reduction when considering IBC hash generation itself, and can save 9–30% of hash generation time even taking into account the proposed second hash generation. It can also reduce the hash-based IBC search time by 14.61%. Furthermore, the proposed algorithm can achieve Bjontegaard delta bit rate savings of −0.66, −0.45 and −0.66% on average for all intra, low-delay, and random access coding structures, respectively.

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 gradient clipping scheme is employed to prevent gradient explosions and enables the network to converge quickly. Experimental results demonstrate that the proposed denoising method can achieve a better performance compared with the state-of-the-art denoising methods. Also, the results indicate that the denoising method has the ability of suppressing different noises with different noise levels by means of one single denoising model.

The following article published in IET Image Processing, Shahid, Muhammad; Taj, Imtiaz A.: ‘Robust retinal vessel segmentation using vessels location map and Frangi enhancement filter’, IET Image Processing, 2018, DOI: 10.1049/ietipr. 2017.0457 on 16th January 2018 has been retracted due to a breach of the IET's Policy in Relation to Plagiarism, Infringement of Copyright and Infringement of Moral Rights and Submission to Multiple Publications. Prof. Imtiaz Ahmed Taj was unaware of and not complicit in any misconduct.

Separating content from noise in historical manuscripts is a fundamental task in digital palaeography. This study presents a fully automated segmentation approach based on the response of Harris corner detectors. The strength and clustering efficiency of the detected corners in the manuscripts are evaluated and used to segment the content from the background and noise. In addition, a manuscript reconstruction technique is proposed from the gradient field using the Poisson method to guide the interpolation. This reconstruction is able to remove noise significantly and hence enhances the contrast of the content thus making it easier for users to read and process these documents. The proposed approaches are evaluated using various standard databases to highlight their effectiveness and robustness to a multitude of noise and writing styles. Subjective and objective evaluations of the experimental results show that these techniques are able to successfully segment and reconstruct a very diverse set of scanned documents. An analysis of the results has also shown that the proposed technique compares favourably against similar counterparts.

Fuzzy C-means algorithm is a popular image segmentation algorithm and many researchers in the past have introduced several improved versions of it. However, they still lacked robustness for segmenting key regions of magnetic resonance (MR) human brain transversal images such as white matter, grey matter, and cerebro spinal fluid with an almost similar contouring of the edges. This study highlights a robust algorithm that is effective in four ways: (i) a distinct contrast between different regions of the human brain, (ii) reduce noise as a result of the contrast stretching procedure, (iii) efficient analysis using both grey scale segmented image and its colour segmented version, and (iv) striking a balance between time consumption and image segmentation results. These objectives have been achieved in two phases: first, MR image segmentation using fast and automatically adjustable Gaussian radial basis function kernel based fuzzy C-means (FAAGKFCM) algorithm which utilises a new objective function that incorporates a similarity measure having both spatial and grey level measure factor (S_ij ), an adaptive factor (w_i ) to remove discrepancy of grey scale image, and using a Gaussian radial basis function (GRBF) kernel-based distance metric in place of the traditional Euclidean distance metric. Second, converting FAAGKFCM segmented image into CIE L*a*b* colour space and successively performing cluster-wise feature extraction using hard k-means clustering.

This study deals with information fusion for image segmentation. The evidence theory (or the Dempster–Shafer theory) allows the modellisation of uncertainty and imprecision in the information as well as the combination of different sources. Here, this approach is used in an unsupervised framework to combine the stochastic watershed segmentation which provides several segmentation results, with a Hessian operator in order to obtain a unique and efficient segmentation. The method is tested on natural images from the Berkeley dataset and evaluated using several evaluation metrics. The fusion results surpass those obtained with stochastic watershed alone.

The author introduces a contour detection method that has relatively low complexity yet still highly accurate. The method is based on extrema detection along the four principal orientations, a trick that can be used to detect not only edges but, in particular, also ridges and rivers. The author makes a comparison to the popular Canny algorithm and shows that the proposed method's only downside is that it cannot detect very high curvatures in edge contours. The method is applied to the task of image classification (satellite images, Caltech-101, etc.) and it is demonstrated that the use of all three contour types (edges, ridges, and rives) improves classification accuracy as opposed to the use of only edge contours. Thus, for image classification, it is more important to extract multiple contour features; the use of the exact detection method appears to play a smaller role. The author's simple method is also appealing for use in individual frames, due to its low complexity.

A novel hybrid fitting energy-based active contours model in the level set framework is proposed. The method fuses the local image fitting term and the global image fitting term to drive the contour evolution, and a special extra term that penalises the deviation of the level set function from a signed distance function is also included in the authors’ method, so the complex and costly reinitialisation procedure is completely eliminated. Their model can efficiently segment the images with intensity inhomogeneity no matter where the initial curve is located in the image. In its numerical implementation, two efficient numerical schemes are used to ensure the sufficient efficiency of the evolution process, one is the algebraic multigrid, which is used for breaking the restrictions on time step; the other is the sparse field method, which is introduced for fast local computation, compared with the traditional schemes, these two strategies can further shorten the time consumption of the evolution process, this allows the level set to quickly reach the true target location. The extensive and promising experimental results on synthetic and real images demonstrate subjectively and objectively the performance of the proposed method.

Quick response (QR) code, one of the two-dimensional barcodes, is now being widely used in all fields. The effectiveness of decoding, however, needs to be improved in real-time application. In most cases, the decoding procedure is time consuming, in which the detection of QR code plays an essential part. Therefore, this study proposes a fast detection method of QR code based on run-length coding: firstly, a novel approach is proposed to detect the minimum region containing position detection pattern (PDP) in QR code. Second, coordinates of central PDP in QR code are calculated by using run-length coding. The highlight in this step is the calculation, which utilises modified Knuth–Morris–Pratt algorithm. By this means, the computational complexity can be reduced tremendously. Finally, QR code can be detected successfully with the coordinates. The experimental results show that the proposed method is time saving and suitable for real-time application.

To solve the homography estimation problem containing outliers and noise, a fast, robust, and accurate method is proposed. In this method, the outliers are rejected based on the differing characteristics of algebraic errors between outliers and inliers, and the homography is estimated by minimising the residual vector. The advantage of this method is in integrating the outlier rejection into the estimation pipeline. The computational complexity of the proposed method is not increased, and the random sample consensus algorithm is not needed to extract the inliers, as was previously necessary. Since the outlier rejection process is based on an algebraic criterion without computing the re-projection error at each step, the speed of the proposed method is improved. Several simulations based on synthetic and real images illustrate the performance of the proposed method in terms of subjective visual quality, objective quality measurement, and computational time. The experimental results demonstrate that the proposed method achieves accurate, efficient and robust homography estimation under different image transformation degrees and different outlier ratios.

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 studies on this technique is limited by the use of handcrafted features. The authors propose a deep MIL method for DR detection, which jointly learns features and classifiers from data and achieves a significant improvement on detecting DR images and their inside lesions. Specifically, a pre-trained convolutional neural network is adapted to achieve the patch-level DR estimation, and then global aggregation is used to make the classification of DR images. Further, the authors propose an end-to-end multi-scale scheme to better deal with the irregular DR lesions. For detection of DR images, they achieve an area under the ROC curve of 0.925 on a subset of a Kaggle dataset, and 0.960 on Messidor. For detection of DR lesions, they achieve an F1-score of 0.924 with sensitivity 0.995 and precision 0.863 on DIARETDB1 using the connected component-level validation.

Image fusion is becoming a promising technique for obtaining a more informative image by combining various source images captured by multimodal imaging systems. The technique finds application in several fields, such as medical imaging, material analysis, satellite imaging, including defence and civilian sectors. This study presents a model, named holoentropy-whale fusion (HWFusion), for the image fusion. Two different multimodal images from magnetic resonance imaging (T1, T1C, T2, FLAIR) are fed into the wavelet transform to convert the images into four subbands. The wavelet coefficients are then fused using a weighted coefficient that utilises two factors, entropy and whale fusion factor, which are calculated using holoentropy and the proposed SP-Whale optimiser, respectively. SP-Whale is an algorithm designed by modifying whale optimisation algorithm with self-adaptive learning particle swarm optimisation and is used for the optimal selection of whale fusion factor. Inverse wavelet transform converts the fused wavelet coefficients obtained by the averaging of fusion factors into fused image. In a comparative analysis, the performance of HWFusion is compared with that of four existing techniques using, mutual information, peak signal-to-noise ratio, and root mean-squared error (RMSE), where it could attain mutual information of 1.8015, RMSE of 1.1701, and peak signal-to-noise ratio of 40.6575.

One of the most effective atmospheric correction methods is dark-object subtraction (DOS) method, where the atmospheric offset can be generated from the image itself. The success of DOS strongly relies on the availability and quality of the dark target. Based on the response to the downwelling irradiances, the most effective dark target would be optically-deep water, which is not always available. It is important to assess the alternative dark targets in the absence of the ideal dark target. This research aimed at comparing the effectiveness of different dark targets for DOS method during mangrove above-ground carbon stock (AGC) mapping and comparing the accuracy with robust atmospheric correction FLAASH method. ALOS AVNIR-2 image was used as the test image, and mangrove forest of Karimunjawa and Kemujan Island was selected as the study area. The comparison covers the quality of healthy mangrove reflectance and the accuracy of vegetation indices for mangrove AGC mapping. The results of this research showed that non-ideal dark targets such as cloud-shadow pixels and the minimum value of the image can be used in the absence of ideal dark target, and DOS method is more efficient and effective than more robust atmospheric correction method.

There are many kinds of defects in pipes, which are difficult to detect with a low degree of automation. In this work, a novel omnidirectional vision inspection system for detection of the morphology defects is presented. An active stereo omnidirectional vision sensor is designed to obtain the texture and depth information of the inner wall of the pipeline in real time. The camera motion is estimated and the space location information of the laser points are calculated accordingly. Then, the faster region proposal convolutional neural network (Faster R-CNN) is applied to train a detection network on their image database of pipe defects. Experimental results demonstrate that system can measure and reconstruct the 3D space of pipe with high quality and the retrained Faster R-CNN achieves fine detection results in terms of both speed and accuracy.

Global abnormal events form unique and distinct motion characteristics and category of anomalies at image level rather than pixel level with less complexity compared to local abnormal events. However, traditional anomaly detection approaches focused more on pixel-level feature extraction from foreground pixels and combine global and local anomaly detection in a single algorithm with equal degree of computational complexity. In this paper, we propose a novel framework for global anomaly detection via block-level feature extraction using context location (CL) and motion-rich STVs (MRSTVs). The histogram of optical flow orientation and motion magnitude features from spatio-temporal volumes (STVs) are used as global feature descriptor to capture motion characteristics of normal and abnormal events. Simple and cost-effective one-class SVM classifier is employed to learn normal behaviour from MRSTVs during training and detect abnormal STVs from test data. Thereafter, a spatio-temporal post-processing technique detects frame-level abnormal behaviour and reduces false alarm rate. We define CL to detect abnormal behaviour in an unexpected region. The proposed approach omits pixel-level feature extraction and background modelling by considering MRSTVs, thus enhances detection rate and reduces computational complexity. We have conducted experiments on widely used UMN and PETS2009 datasets to compare the performance of proposed approach with existing methods.

The discrete Tchebychef moments (DTMs) are a set of discrete orthogonal moments applied in the field of objects description and image analysis. They show great precision in providing precise features representation capability for any given image. While using these models seems to be the preferred way for object description, the trade-off is the considerable calculation time they require. The goal is to explore the feasibility of preparing and using a pre-calculated data structure for the computation of the DTMs. To make the pre-calculated data structure possible, the sizing property of the DTMs is used. Experimental results show the equal accuracy of the calculated moments compared with the classic calculation process while realising a significant decrease in calculation time.

Estimating the three-dimensional (3D) motion from sparse laser point clouds is a highly challenging endeavour facing computer and robotic vision engineers. In this study, a novel method is proposed for robustly estimating the scene flow from a laser scanner assisted by a camera. Conditional random field (CRF) is constructed by a spatial structure of point clouds, the energy of which is minimised by a synchronous calibrated image. With the high frame rate of a laser scanner, the authors’ method allows for estimating the potential motion field as the CRF label. The authors ran an experiment on a public dataset to demonstrate that their method can accurately estimate rigid motion in outdoor scenes. They also tested the method on a laser scanner and omni-directional camera system to find that it also accurately estimates the rigid and semi-rigid motion of objects in a controlled indoor environment.