Remote sensing images play a critical role in modern Earth Observation missions, but the interpretations of remote sensing images are easily to be obstructed by the existence of cloud and haze. In this paper, a novel end-to-end generative Single-image thin Cloud and haze Removal Network, referred to as SCR-net, is proposed. The designed SCRnet is a generative model based on Conditional Generative Adversarial Nets (C-GANs), which uses a generator network G to learn the conditional distribution of the cloud-free data and a multi-scale discriminator D to distinguish between real and fake images. Through supervised training, the generator G can predict a cloud-free image using the input noise and a cloud-contaminated image of the same spot. An adversarial minimax objective is used to drive G to learn the distribution of real cloud-free data, and a distance objective in both feature space and image space ensures the quality of the generated image. Quantitative and qualitative experiments are conducted on the synthesized images and real images. Results reveal that the proposed method can effectively remove even and uneven thin cloud in remote sensing images of various scenes, along with good color consistency.
Content-based image retrieval (CBIR) has been widely researched for medical images. In application of histo- pathological images, there are two issues that need to be carefully considered. The one is that the digital slide is stored in a spatially continuous image with a size of more than 10K x 10K pixels. The other is that the size of query image varies in a large range according to different diagnostic conditions. It is a challenging work to retrieve the eligible regions for the query image from the database that consists of whole slide images (WSIs). In this paper, we proposed a CBIR framework for the WSI database and size-scalable query images. Each WSI in the database is encoded and stored in a matrix of binary codes. When retrieving, the query image is first encoded into a set of binary codes and analyzed to pre-choose a set of regions from database using hashing method. Then a multi-binary-code-based similarity measurement based on hamming distance is designed to rank proposal regions. Finally, the top relevant regions and their locations in the WSIs along with the diagnostic information are returned to assist pathologists in diagnoses. The effectiveness of the proposed framework is evaluated in a fine-annotated WSIs database of epithelial breast tumors. The experimental results show that proposed framework is both effective and efficiency for content-based whole slide image retrieval.
Hair removal from skin melanoma image is one of the key problems for the precise segmentation and analysis of the skin
malignant melanoma. In this paper, an automatically hair removal algorithm in dermoscopy images of pigmented skin
lesions is proposed. This algorithm includes three steps: firstly, the melanoma image with hairs are enhanced by
morphologic closing-based top-hat operator and then segmented through statistic threshold; secondly, the hairs are
extracted based on the elongate of connected region; thirdly, the hair-occluded information is repaired by replacing the
hair pixels with the nearby non-hair pixels. As a matter of fact, with the morphologic closing-based top-hat operator both
strong and weak hairs can be enhanced simultaneously, and the elongate state of band-like connected region can be
correctly described by the elongate function proposed in this paper so as to measure the hair effectively. Therefore, the
unsupervised hair removal problem in dermoscopy melanoma image can be resolved very well through combining the
hair extraction with information repair. The experiment results show that various hairs can be extracted accurately and
the repaired effect of textures can satisfy the requirement of medical diagnosis.
Human skin gradually lose its tension and becomes very dry as time flies by. Use of cosmetics is effective to prevent skin aging. Recently, there are many choices of products of cosmetics. To show their effects, It is desirable to develop a way to evaluate quantificationally skin surface condition. In this paper, An automatic skin evaluating method is proposed. The skin surface has the pattern called grid-texture. This pattern is composed of the valleys that spread vertically, horizontally, and obliquely and the hills separated by them. Changes of the grid are closely linked to the skin surface condition. They can serve as a good indicator for the skin condition. By measuring the skin grid using digital image processing technologies, we can evaluate skin surface about its aging, health, and alimentary status. In this method, the skin grid is first detected to form a closed net. Then, some skin parameters such as Roughness, tension, scale and gloss can be calculated from the statistical measurements of the net. Through analyzing these parameters, the condition of the skin can be monitored.
In this paper, an effective medical micro-optical image matching algorithm based on relativity is described. The algorithm includes the following steps: Firstly, selecting a sub-area that has obvious character in one of the two images as standard image; Secondly, finding the right matching position in the other image; Thirdly, applying coordinate transformation to merge the two images together. As a kind of application of image matching in medical micro-optical image, this method overcomes the shortcoming of microscope whose visual field is little and makes it possible to watch a big object or many objects in one view. Simultaneously it implements adaptive selection of standard image, and has a satisfied matching speed and result.
An effective immune cell image segmentation algorithm based on mathematical morphology is presented in this paper. In order to get better segmentation results in addition to the morphology based watershed growth algorithm the histogram potential function is involved, which means, the image spectral information is combined with spacial information. How to get the exact segmentation result is a major issue for immune cell image analysis. Obtaining an effective and credible marker is a crucial step of watershed segmentation. By involving the histogram potential function, the markers suitable for watershed segmentation can be clearly improved and the segmentation result is quite consistent with human vision and also the segmentation speed and repeatability are quite acceptable.