Channel (digital image)

About: Channel (digital image) is a research topic. Over the lifetime, 7211 publications have been published within this topic receiving 69974 citations.

Single image defogging

Abstract: Bad weather, such as fog and haze can significantly degrade the visibility of a scene. In order to overcome it, some approaches have been proposed. In this paper, we proposed a novel defogging algorithm just based on a single image. Firstly, we employ the method of dark channel prior as a foundation of our algorithm. After experimental analysis about the dark channel prior haze removal, we find that although dark channel prior behaves well in most situations, it also results in larger saturation values in some specific situations. Then, focusing on these situations, we propose an iterative algorithm to adjust the color distortion effected by higher saturation. Actually, this kind of global or local rectification can achieve a relatively ideal compromise between natural color and image definition. Experiments show that the algorithm proposed in this paper is good at dealing with the similarity-to-atmospheric-light objects defogging problem.

Person reidentification using quaternionic local binary pattern

Abstract: Person reidentification is to identify the persons observed in nonoverlapping camera networks. Most existing methods usually extract features from the red, green, and blue color channels of images individually. They, however, neglect the connections between each color component in the image. To overcome this problem, a novel quaternionic local binary pattern (QLBP) is proposed for person reidentification in this paper. In the proposed QLBP, each pixel in a color image is represented by a quaternion so that we can handle all color components in a holistic way. A novel pseudo-rotation of quaternion (PRQ) is proposed to rank two quaternions. Some properties of PRQ are also discussed. After a QLBP coding, the local histograms are extracted and used as features. Experiments on two public benchmarking datasets, ETHZ and i-LIDS MCTS, are carried out to evaluate the QLBP performance. Comparison results show that the QLBP outperforms several stat-of-art methods for person reidentification.

Cycle-IR: Deep Cyclic Image Retargeting

Abstract: Supervised deep learning techniques have achieved great success in various fields due to getting rid of the limitation of handcrafted representations. However, most previous image retargeting algorithms still employ fixed design principles such as using gradient map or handcrafted features to compute saliency map, which inevitably restricts its generality. Deep learning techniques may help to address this issue, but the challenging problem is that we need to build a large-scale image retargeting dataset for the training of deep retargeting models. However, building such a dataset requires huge human efforts. In this paper, we propose a novel deep cyclic image retargeting approach, called Cycle-IR, to firstly implement image retargeting with a single deep model, without relying on any explicit user annotations. Our idea is built on the reverse mapping from the retargeted images to the given images. If the retargeted image has serious distortion or excessive loss of important visual information, the reverse mapping is unlikely to restore the input image well. We constrain this forward-reverse consistency by introducing a cyclic perception coherence loss. In addition, we propose a simple yet effective image retargeting network (IRNet) to implement the image retargeting process. Our IRNet contains a spatial and channel attention layer, which is able to discriminate visually important regions of input images effectively, especially in cluttered images. Given arbitrary sizes of input images and desired aspect ratios, our Cycle-IR can produce visually pleasing target images directly. Extensive experiments on the standard RetargetMe dataset show the superiority of our Cycle-IR.

Graph-based segmentation integrating visible and nir information

Abstract: A method for segmenting an image includes extracting unary potentials for pixels of the input image. These can be based for each of a set of possible labels, on information for a first channel in the image, such as in the visible range of the spectrum. Pairwise potentials are extracted for neighboring pairs of pixels of the image. These can be based on information for a second channel in the image, such as in the infrared range of the spectrum. An objective function is optimized over pixels of the input image to identify labels for the pixels. The objective function is based on a combination of ones of the extracted unary and pairwise potentials. The image is then segmented, based on the identified pixel labels. The method and system can provide an improvement in segmentation over methods which use only the visible information.

Attention boosted bilinear pooling for remote sensing image retrieval

Abstract: Remote sensing image retrieval is to find the most identical or similar images to a query image in the vast archive of remote sensing images. A key process is to extract the most distinctive features. In this study, we introduce a second-order pooling named compact bilinear pooling (CBP) into convolutional neural networks (CNNs) for remote sensing image retrieval. The retrieval algorithm has three stages, pretraining, fine-tuning and retrieval. In the pretraining stage, two classic CNN structures, VGG16 and ResNet34, are pretrained respectively with the ImageNet consisting of close-range images. A CBP layer is introduced before the fully connected layers in the two networks. To extract globally consistent representations, a channel and spatial integrated attention mechanism is proposed to refine features from the last convolution layer and the features are used as the input of the CBP. In the fine-tuning stage, the new network is fine-tuned on a remote sensing dataset to train discriminable features. In the retrieval stage, the network, with fully connected layers being replaced by a PCA (principal component analysis) module, is applied to new remote sensing datasets. Our retrieval algorithm with the combination of CBP and PCA obtained the best performance and outperformed several mainstream pooling or encoding methods such as full-connected layer, IFK (Improved Fisher Kernel), BoW (Bag-of-Words) and maxpooling, etc. The channel and spatial attention mechanism contributes to the CBP based retrieval method and obtained the best performance on all the datasets, as well as outperformed several recent attention methods. Source code is available at http://study.rsgis/whu.edu.cn/pages/download.