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With the development of computer vision, high quality images with rich information have great research potential in both daily life and scientific research. However, due to different lighting conditions, surrounding noise and other reasons, the image quality is different, which seriously affects people's discrimination of the information in the image, thus causing unnecessary conflicts and results. Especially in the dark, the images captured by the camera are difficult to identify, and the smart system relies heavily on high-quality input images. The image collected in low-light environment has the characteristic with high noise and color distortion, which makes it difficult to utilize the image and can not fully explore the rich value information of the image. In order to improve the quality of low-light image, this paper proposes a Heterogenous low-light image enhancement method based on DenseNet generative adversarial network. Firstly, the generative network of generative adversarial network is realized by using DenseNet framework. Secondly, the feature map from low light image to normal light image is learned by using the generative adversarial network. Thirdly, the enhancement of low-light image is realized. The experimental results show that, in terms of PSNR, SSIM, NIQE, UQI, NQE and PIQE indexes, compared with the state-of-the-art enhancement algorithms, the values are ideal, the proposed method can improve the image brightness more effectively and reduce the noise of enhanced image. [ABSTRACT FROM AUTHOR]

In recent years, with the rapid development of artificial intelligence, image caption has gradually attracted the attention of many researchers in the field of artificial intelligence and has become an interesting and arduous task. Image caption, automatically generating natural language descriptions according to the content observed in an image, is an important part of scene understanding, which combines the knowledge of computer vision and natural language processing. The application of image caption is extensive and significant, for example, the realization of human-computer interaction. This paper summarizes the related methods and focuses on the attention mechanism, which plays an important role in computer vision and is recently widely used in image caption generation tasks. Furthermore, the advantages and the shortcomings of these methods are discussed, providing the commonly used datasets and evaluation criteria in this field. Finally, this paper highlights some open challenges in the image caption task. [ABSTRACT FROM AUTHOR]

Saliency detection is still very challenging in computer vision and image processing. In this paper, we propose a novel visual saliency detection framework via bagging-based saliency distribution learning (BSDL). Given an input image, we firstly segment it into superpixels as basic units. Then two prior knowledge containing background prior and center prior are integrated to generate an initial prior map, which is used to select training samples from all superpixels to train the BSDL model. Specifically, the BSDL contains two stages: In the first stage, we use bagging-based sampling method to train K saliency classifiers from all training samples. K saliency classifiers are used to predict each superpixel saliency value. In the second stage, we aim to learn a saliency distribution model, whose goal is to infer the relationship between each classifier and each superpixel. i.e., for each superpixel, the BSDL not only trains K saliency classifiers to predict its saliency value, but also infers the reliability of using each saliency classifier to predict its saliency value. As a result, each superpixel's saliency value is determined by its K prediction saliency values and saliency distribution. After the BSDL, we propose a so called foreground consistency saliency optimization framework (FCSO) to further refine saliency map obtained by BSDL. To improve computation efficiency, a prejudgment rule is proposed to evaluate the quality of saliency map obtained by BSDL, which is used to decide whether the FCSO is needed for input image. Experimental results on four public datasets demonstrate the superiority of the proposed method than other state-of-the-art methods. • We propose a novel bagging-based saliency distribution learning framework for visual saliency detection. • We propose a so called foreground consistency saliency optimization framework to further refine saliency map. • An effective prejudgment mechanism is developed to improve computational efficiency. • Experimental results on four datasets indicate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Recently, single image super-resolution (SISR) has been widely applied in the fields of multimedia and computer vision communities and obtained remarkable performance. However, most current methods ignore to utilize multi-granularity features of low-resolution (LR) image to further improve the SISR performance. And the channel and spatial features obtained from original LR images are treated equally, resulting in unnecessary computations for abundant uninformative features, thereby hindering the representational ability of super-resolution (SR) models. In this paper, we present a novel Multi-Granularity Pyramid Attention Network (MGPAN) which fully exploits the multi-granularity perception and attention mechanisms to improve the quality of reconstructed images. We design a multi-branch dilated convolution layer with varied kernels corresponding to receptive fields of different sizes to modulate multi-granularity features for adaptively capturing more important information. Moreover, a novel spatial pyramid pooling attention (SPPA) module is constructed to integrate the channel-wise and multi-scale spatial information, which is beneficial to compute the response values from the multi-scale regions of each neuron, and then establish the accurate mapping from low to high dimensional solution space. Besides, for long-short-term information preservation and information flow enhancement, we adopt the short, long, and global skip connection structures to concatenate and fuse the states of each module, which can improve the SR network performance effectively. Extensive experiments on several standard benchmark datasets show that the proposed MGPAN can provide state-of-the-art or even better performance in both quantitative and qualitative measurements. [ABSTRACT FROM AUTHOR]