Distortion-Free Data Embedding Scheme for High Dynamic Range Images

Distortion-free data embedding is a technique which can assure that not only the secret data is correctly extracted but also the cover media is recovered without any distortion after secret data is extracted completely. Because of these advantages, this technique attracts the attention of many researchers. In this paper, a new distortion-free data embedding scheme for high dynamic range （HDR） images is proposed. By depending on Cartesian product, this scheme can obtain higher embedding capacity while maintaining the exactly identical cover image and stego image when using the tone mapping algorithms. In experimental results, the proposed scheme is superior to Yu et aL＇s scheme in regard to the embedding rate——an average embedding rate of 0.1355 bpp compared with Yn et aL＇s scheme （0.1270 bpp）.

Multi-exposure fusion for high dynamic range scene

Due to the existing limited dynamic range a camera cannot reveal all the details in a high-dynamic range scene. In order to solve this problem,this paper presents a multi-exposure fusion method for getting high quality images in high dynamic range scene. First,a set of multi-exposure images is obtained by multiple exposures in a same scene and their brightness condition is analyzed. Then,multi-exposure images under the same scene are decomposed using dual-tree complex wavelet transform( DT-CWT),and their low and high frequency components are obtained. Weight maps according to the brightness condition are assigned to the low components for fusion. Maximizing the region Sum Modified-Laplacian( SML) is adopted for high-frequency components fusing. Finally,the fused image is acquired by subjecting the low and high frequency coefficients to inverse DT-CWT.Experimental results show that the proposed approach generates high quality results with uniform distributed brightness and rich details. The proposed method is efficient and robust in varies scenes.

Novel Logarithmic Active Pixel Sensor with High Dynamic Range and High Output Swing

The logarithmic response complementary metal oxide semiconductor （CMOS） image sensor provides a wide dynamic range, but its drawback is the lack of simple fixed pattern noise（FPN） cancellation scheme. Designed is a novel logarithmic active pixel sensor（APS） with high dynamic range and high output swing. Firstly, the operation principle of mixed-model APS is introduced. The pixel can work in three operation modes by choosing the proper control signals. Then, FPN sources of logarithmic APS are analyzed, and double-sampled technique is implemented to reduce FPN. Finally, according to the simulation results, layout is designed and has passed design rule check（DRC）, electronic rule eheck（ERC） and layout versus schematic（LVS） verifications, and the post-simulation results are basically in agreement with the simulation results. Dynamic range of the new logarithmic APS can reach about 140 dB; and the output swing is about 750 inV. Results show that by using double sampled technique, most FPN is eliminated and the dynamic range is enhanced.

Shape recovery using high dynamic range images

An effective method for object shape recovery using HDRIs （high dynamic range images） is proposed. The radiance values of each point on the reference sphere and target object are firstly calculated, thus the set of candidate normals of each target point are found by comparing its radiance to that of each reference sphere point. In single-image shape recovery, a smoothness operation is applied to the target normals to obtain a stable and reasonable result; while in photometric stereo, radiance vectors of reference and target objects formed due to illuminations under different fight source directions are directly compared to get the most suitable target normals. Finally, the height values can be recovered from the resulting normal field. Because diffuse and specular reflection are handled in an unified framework with radiance, our approach eliminates the limitation presented in most recovery strategies, i.e., only Lambertian model can be used. The experiment results from the real and synthesized images show the performance of our approach.

Hawk‐eye‐inspired perception algorithm of stereo vision for obtaining orchard 3D point cloud navigation map

The binocular stereo vision is the lowest cost sensor for obtaining 3D information.Considering the weakness of long‐distance measurement and stability,the improvement of accuracy and stability of stereo vision is urgently required for application of precision agriculture.To address the challenges of stereo vision long‐distance measurement and stable perception without hardware upgrade,inspired by hawk eyes,higher resolution perception and the adaptive HDR(High Dynamic Range)were introduced in this paper.Simulating the function from physiological structure of‘deep fovea’and‘shallow fovea’of hawk eye,the higher resolution reconstruction method in this paper was aimed at ac-curacy improving.Inspired by adjustment of pupils,the adaptive HDR method was proposed for high dynamic range optimisation and stable perception.In various light conditions,compared with default stereo vision,the accuracy of proposed algorithm was improved by 28.0%evaluated by error ratio,and the stability was improved by 26.56%by disparity accuracy.For fixed distance measurement,the maximum improvement was 78.6%by standard deviation.Based on the hawk‐eye‐inspired perception algorithm,the point cloud of orchard was improved both in quality and quantity.The hawk‐eye‐inspired perception algorithm contributed great advance in binocular 3D point cloud recon-struction in orchard navigation map.

Use of Local Region Maps on Convolutional LSTM for Single-Image HDR Reconstruction

Low dynamic range(LDR)images captured by consumer cameras have a limited luminance range.As the conventional method for generating high dynamic range(HDR)images involves merging multiple-exposure LDR images of the same scene(assuming a stationary scene),we introduce a learning-based model for single-image HDR reconstruction.An input LDR image is sequentially segmented into the local region maps based on the cumulative histogram of the input brightness distribution.Using the local region maps,SParam-Net estimates the parameters of an inverse tone mapping function to generate a pseudo-HDR image.We process the segmented region maps as the input sequences on long short-term memory.Finally,a fast super-resolution convolutional neural network is used for HDR image reconstruction.The proposed method was trained and tested on datasets including HDR-Real,LDR-HDR-pair,and HDR-Eye.The experimental results revealed that HDR images can be generated more reliably than using contemporary end-to-end approaches.

Comparison of Color and Grayscale Simulated TV-Graphics in Rendezvous and Docking

Subjective evaluation is one of the most important methods of assessing the fidelity of a virtual dynamic scene,whose results could be seen as a reference to improve a rendering method.In the present study,we apply a rendering method to the color rendezvous and docking(RVD) scene,where one shadow algorithm and three tone mapping operator algorithms are used to process the scene based on the distance between two spacecrafts.The grayscale RVD scene is transformed from the color RVD scene,and each RVD scene is desired to make comparisons with the real RVD-video recorded in SZ-10 mission.In addition,we perform an evaluation on 36 subjects to compare the results.The results show that shadow effects have important roles in RVD virtual scenes,where they enhance the sense of realism and immersion.Shadows,high brightness,contrast and luminance are highly correlated with the overall preference,and the grayscale scene receives better evaluations than the color scene.The analysis of these conclusions is vital for improving the algorithms used to render color virtual space scenes when virtual scenes for astronaut training in Chinese space station mission are prepared.