Low Dynamic Images Research Articles

Multi-Exposure Fusion of Gray Images Under Low Illumination Based on Low-Rank Decomposition

Existing multi-exposure fusion (MEF) algorithms for gray images under low-illumination cannot preserve details in dark and highlighted regions very well, and the fusion image noise is large. To address these problems, an MEF method is proposed. First, the latent low-rank representation (LatLRR) is used on low-dynamic images to generate low-rank parts and saliency parts to reduce noise after fusion. Then, two components are fused separately in Laplace multi-scale space. Two different weight maps are constructed according to features of gray images under low illumination. At the same time, an energy equation is designed to obtain the optimal ratio of different weight factors. An improved guided filtering based on an adaptive regularization factor is proposed to refine the weight maps to maintain spatial consistency and avoid artifacts. Finally, a high dynamic image is obtained by the inverse transform of low-rank part and saliency part. The experimental results show that the proposed method has advantages both in subjective and objective evaluation over state-of-the-art multi-exposure fusion methods for gray images under low-illumination imaging.

A Contribution Algorithm from LDRI to HDRI

High dynamic range image (HDRI) which is combined with low dynamic range image (LDRI) needs to be mapped to a low dynamic area to display. In the process of mapping, it is impossible to determine the contribution of low dynamic image sequences in the display images, so that it results in a problem that the low dynamic images cannot be accurately selected. In this paper, for the first time, a contribution algorithm from LDRI to HDRI according to the corresponding response curve of the camera is proposed.

A Novel Multi-Exposure Image Fusion Method Based on Adaptive Patch Structure.

Multi-exposure image fusion methods are often applied to the fusion of low-dynamic images that are taken from the same scene at different exposure levels. The fused images not only contain more color and detailed information, but also demonstrate the same real visual effects as the observation by the human eye. This paper proposes a novel multi-exposure image fusion (MEF) method based on adaptive patch structure. The proposed algorithm combines image cartoon-texture decomposition, image patch structure decomposition, and the structural similarity index to improve the local contrast of the image. Moreover, the proposed method can capture more detailed information of source images and produce more vivid high-dynamic-range (HDR) images. Specifically, image texture entropy values are used to evaluate image local information for adaptive selection of image patch size. The intermediate fused image is obtained by the proposed structure patch decomposition algorithm. Finally, the intermediate fused image is optimized by using the structural similarity index to obtain the final fused HDR image. The results of comparative experiments show that the proposed method can obtain high-quality HDR images with better visual effects and more detailed information.

Algorithm of high-dynamic fusion image gray characterization based on variable energy

X-ray imaging based on variable energy can expand the dynamic range of the imaging system and perfectly show the structure information of the detection objects, by acquiring and fusing the image sequences. However, the fusion method is ordinarily based on image quality optimization, and neglects the gray mapping accuracy of the actual high dynamic imaging. It cannot guarantee the physical matching between the image information and actual structure information. Therefore, in this paper we propose an X-ray image gray characterization algorithm of high dynamic fusion based on variable energy. First, take a standard wedge block as test object, and use the fusion image of low dynamic image sequences as input data. The output data are the actual high dynamic image. Then establish the X-ray imaging gray characterization model by neural network training. At the same time, because the attenuation coefficients of different heterogeneous materials are different, a modified model of physical characterization is established to achieve a correct characterization of real object. Finally, experiments by 12 bit and 16 bit imaging systems acquire the variable voltage image sequences using 12 bit detector. After image fusion, image mapping and gray level correction, the output image not only achieves the same effect of 16 bit detector, but also satisfies the gray relation. Also this method can effectively expand the dynamic range of the imaging system.

Learning to display high dynamic range images

In this paper, we present a learning-based image processing technique. We have developed a novel method to map high dynamic range scenes to low dynamic range images for display in standard (low dynamic range) reproduction media. We formulate the problem as a quantization process and employ an adaptive conscience learning strategy to ensure that the mapped low dynamic range displays not only faithfully reproduce the visual features of the original scenes, but also make full use of the available display levels. This is achieved by the use of a competitive learning neural network that employs a frequency sensitive competitive learning mechanism to adaptively design the quantizer. By optimizing an L 2 distortion function, we ensure that the mapped low dynamic images preserve the visual characteristics of the original scenes. By incorporating a frequency sensitive competitive mechanism, we facilitate the full utilization of the limited displayable levels. We have developed a deterministic and practicable learning procedure which uses a single variable to control the display result. We give a detailed description of the implementation procedure of the new learning-based high dynamic range compression method and present experimental results to demonstrate the effectiveness of the method in displaying a variety of high dynamic range scenes.