Explore the vast range of titles available.

Owing to the flexible architectures of deep convolutional neural networks (CNNs) are successfully used for image denoising. However, they suffer from the following drawbacks: (i) deep network architecture is very difficult to train. (ii) Deeper networks face the challenge of performance saturation. In this study, the authors propose a novel method called enhanced convolutional neural denoising network (ECNDNet). Specifically, they use residual learning and batch normalisation techniques to address the problem of training difficulties and accelerate the convergence of the network. In addition, dilated convolutions are used in the proposed network to enlarge the context information and reduce the computational cost. Extensive experiments demonstrate that the ECNDNet outperforms the state-of-the-art methods for image denoising.

In this work, the authors develop a working software-based approach named ‘linearly quantile separated histogram equalisation-grey relational analysis’ for mammogram image (MI). This approach improves overall contrast (local and global) of given MI and segments breast-region with a specific end goal to acquire better visual elucidation, examination, and grouping of mammogram masses to help radiologists in settling on more precise choices. The fundamental commitment of this work is to demonstrate that results of good quality of breast-region segmentation can be accomplished from basic breast-region segmentation if the input image has good contrast and a better interpretation of hidden details. They have evaluated the proposed strategy for MIAS-MIs. Experimental results have shown that the proposed approach works better than state-of-the-art.

The object contour is a significant cue for identifying and categorising objects. The current work is motivated by indicative researches that attribute object contours to edge information. The spatial continuity exhibited by the edge pixels belonging to the object contour make these different from the noisy edge pixels belonging to the background clutter. In this study, the authors seek to quantify the object contour from a relative count of the adjacent edge pixels that are oriented in the four possible directions, and measure using exponential functions the continuity of each edge over the next adjacent pixel in that direction. The resulting computationally simple, low-dimensional feature set, called as ‘edge continuity features’, can successfully distinguish between object contours and at the same time discriminate intra-class contour variations, as proved by the high accuracies of object recognition achieved on a challenging subset of the Caltech-256 dataset. Grey-to-RGB template matching with City-block distance is implemented that makes the object recognition pipeline independent of the actual colour of the object, but at the same time incorporates colour edge information for discrimination. Comparison with the state-of-the-art validates the efficiency of the proposed approach.

The artistic style transfer of images aims to synthesise novel images by combining the content of one image with the style of another, which is a long-standing research topic and already has been widely applied in real world. However, defining the aesthetic perception from the human visual system is a challenging problem. In this study, the authors propose a novel method for automatic visual perception style transfer. First, they render a novel saliency detection algorithm to automatically perceive the visual attention of an image. Then, different from conventional style transfer algorithm in which style transferring is applied uniformly across all image regions, the authors apply a saliency algorithm to guide the style transferring process, enabling different types of style transferring to occur in different regions. Extensive experiments show that the proposed saliency detection algorithm and the style transfer algorithm are superior in performance and efficiency.

In this study, a fast object detection algorithm based on binary deep convolution neural networks (CNNs) is proposed. Convolution kernels of different sizes are used to predict classes and bounding boxes of multi-scale objects directly in the last feature map of a deep CNN. In this way, rapid object detection with acceptable precision loss is achieved. In addition, binary quantisation for weight values and input data of each layer is used to squeeze the networks for faster object detection. Compared to full-precision convolution, the proposed binary deep CNNs for object detection results in 62 times faster convolutional operations and 32 times memory saving in theory, what's more, the proposed method is easy to be implemented in embedded computing systems because of the binary operation for convolution and low memory requirement. Experimental results on Pascal VOC2007 validate the effectiveness of the authors’ proposed method.

The learning-based super-resolution reconstruction method inputs a low-resolution image into a network, and learns a non-linear mapping relationship between low-resolution and high-resolution through the network. In this study, the multi-scale super-resolution reconstruction network is used to fuse the effective features of different scale images, and the non-linear mapping between low resolution and high resolution is studied from coarse to fine to realise the end-to-end super-resolution reconstruction task. The loss of some features of the low-resolution image will negatively affect the quality of the reconstructed image. To solve the problem of incomplete image features in low-resolution, this study adopts the multi-scale super-resolution reconstruction method based on guided image filtering. The high-resolution image reconstructed by the multi-scale super-resolution network and the real high-resolution image are merged by the guide image filter to generate a new image, and the newly generated image is used for secondary training of the multi-scale super-resolution reconstruction network. The newly generated image effectively compensates for the details and texture information lost in the low-resolution image, thereby improving the effect of the super-resolution reconstructed image.Compared with the existing super-resolution reconstruction scheme, the accuracy and speed of super-resolution reconstruction are improved.

Cross-modal retrieval has been recently proposed to find an appropriate subspace, where the similarity across different modalities such as image and text can be directly measured. In this study, different from most existing works, the authors propose a novel model for cross-modal retrieval based on a domain-adaptive limited text space (DALTS) rather than a common space or an image space. Experimental results on three widely used datasets, Flickr8K, Flickr30K and Microsoft Common Objects in Context (MSCOCO), show that the proposed method, dubbed DALTS, is able to learn superior text space features which can effectively capture the necessary information for cross-modal retrieval. Meanwhile, DALTS achieves promising improvements in accuracy for cross-modal retrieval compared with the current state-of-the-art methods.

Saliency map estimation in computer vision aims to estimate the locations where people gaze in images. Since people tend to look at objects in images, the parameters of the model pre-trained on ImageNet for image classification are useful for the saliency map estimation. However, there is no research on the relationship between the image classification accuracy and the performance of the saliency map estimation. In this study, it is shown that there is a strong correlation between image classification accuracy and saliency map estimation accuracy. The authors also investigated the effective architecture based on multi-scale images and the up-sampling layers to refine the saliency-map resolution. The model achieved the state-of-the-art accuracy on the PASCAL-S, OSIE, and MIT1003 datasets. In the MIT saliency benchmark, the model achieved the best performance in some metrics and competitive results in the other metrics.

Sparse representation has been widely applied to multi-focus image fusion in recent years. As a key step, the construction of an informative dictionary directly decides the performance of sparsity-based image fusion. To obtain sufficient bases for dictionary learning, different geometric information of source images is extracted and analysed. The classified image bases are used to build corresponding subdictionaries by principle component analysis. All built subdictionaries are merged into one informative dictionary. Based on constructed dictionary, compressive sampling matched pursuit algorithm is used to extract corresponding sparse coefficients for the representation of source images. The obtained sparse coefficients are fused by Max-L1 fusion rule first, and then inverted to form the final fused image. Multiple comparative experiments demonstrate that the proposed method is competitive with other the state-of-the-art fusion methods.

The measurement matrix which plays an important role in compressed sensing has got a lot of attention. However, the existing measurement matrices ignore the energy concentration characteristic of the natural images in the sparse domain, which can help to improve the sensing efficiency and the construction efficiency. Here, the authors propose a simple but efficient measurement matrix based on the Hadamard matrix, named Hadamard-diagonal matrix (HDM). In HDM, the energy conservation in the sparse domain is maximised. In addition, considering the reconstruction performance can be further improved by decreasing the mutual coherence of the measurement matrix, an effective optimisation strategy is adopted in order to reducing the mutual coherence for better reconstruction quality. The authors conduct several experiments to evaluate the performance of HDM and the effectiveness of optimisation algorithm. The experimental results show that HDM performs better than other popular measurement matrices, and the optimisation algorithm can improve the performance of not only the HDM but also the other popular measurement matrices.