The curve-based lighting adjustment technique is widely used in fields such as photography and image processing. The deep learning-based lighting curve adjustment method has shown excellent performance in the field of low-light image enhancement. However, existing curve-based deep learning methods tend to use complex mathematical formulas to define the curve model and add a large number of regularization constraints to ensure that the curve conforms to real physical scenes. This limits the flexibility of the lighting curve, making it unable to accurately enhance brightness for low-light images, resulting in problems such as regional color distortion and overall color bias. To solve this problem, we propose a novel low-light image enhancement model called Discrete Brightness Curve Estimation (DBCE-Net). In DBCE-Net, we introduce a new method for defining curves to enhance regional illumination more effectively. At the same time, we propose a discrete parameter calculation network based on mutual attention mechanism to estimate the discrete brightness adjustment curve from low-light images. Finally, we use a multi-scale denoising network to handle noise introduced by brightness enhancement in shadow areas. Extensive experiments on various datasets have demonstrated that our DBCENet achieves competitive performance in terms of objective quantitative metrics and subjective visual quality evaluation.
With the rapid development of 3D capture technologies, point cloud has been widely used in many emerging applications such as augmented reality, autonomous driving, and 3D printing. However, point cloud, used to represent real world objects in these applications, may contain millions of points, which results in huge data volume. Therefore, efficient compression algorithms are essential for point cloud when it comes to storage and real-time transmission issues. Specially, the attribute compression of point cloud is still challenging owing to the sparsity and irregular distribution of corresponding points in 3D space. In this paper, we present a novel point cloud attribute compression scheme based on inter-prediction of blocks and graph Laplacian transforms for attributes residual. Firstly, we divide the entire point cloud into adaptive sub-clouds via K-means based on the geometry to acquire sub-clouds, which enables efficient representation with less cost. Secondly, the sub-clouds are divided into two parts, one is the attribute means of the sub clouds, another is the attribute residual by removing the means. For the attribute means, we use inter-prediction between sub-clouds to remove the attribute redundancy, and the attribute residual is encoded after graph Fourier transforming. Experimental results demonstrate that the proposed scheme is much more efficient than traditional attribute compression schemes.
High-quality depth information is urgently required with their increasingly wide application in many real-world multimedia fields. However, due to the limitation of depth sensing technology, the captured depth map in practice usually owns low resolution and poor quality, which limits its practical application. As we all know, consistency between high-quality color images and low-quality depth maps achieves good effects in depth super-resolution. But the edge inconsistency also limits the recovery of depth map. Inspired by the geometric relationship between surface normal of a 3D scene and their distance from camera, we discover that there are more consistency between surface normal map and depth map in the edge areas. Meanwhile, surface normal map can provide more spatial geometric constraints for depth map reconstruction, for both of them are special images with spatial information, which we called 2.5D images. In this paper, we propose a unified framework, Normal Data Guided Depth Map Restoration with Edge-Preserving Smoothing Regularization (NDEPS) method, via joint spatial domain and gradient domain regularization, one characterizing the relationship between surface normal data and depth in the spatial domain and another edge-aware constraint in the gradient domain. The proposed NDEPS method formulates a constrained optimization problem that can be solved by an iterative conjugate gradient(CG) algorithm. Extensive quantitative and qualitative evaluations compared with state-of-the-art depth recovery methods show the effectiveness and superiority of our method.
As a key research material, graphene has high thermal expansion coefficient and heat conductivity. FBG with cladding by coated graphene can effectively improve temperature sensing characteristics according to the temperature sensing principle of FBG. Firstly, after removing the coating on FBG surface with acetone and stripping pliers, graphene nanosheets were deposited on the cladding material of FBG by a simple evaporative deposition method. The temperature sensing sensitivity 13.05 pm/°C was obtained in experiment after linear fitting of data, which was about 67% higher than that of bare FBG of 7.82pm/°C. Secondly, in order to improve the purity, compactness and controllable thickness of the coatings, we deposit graphene films of FBG cladding with coating process in vacuum by pulsed laser deposition (PLD) technology. Finally, the temperature sensing sensitivity of the FBG sensor coated with graphene thin film by PLD reached 17.31 pm/°C, which was about 120% higher than that before deposition. Moreover, the whole temperature sensing system of FBG with cladding graphene film has simple structure and high practicability
We propose a high-sensitivity temperature sensor with a long-period fiber grating (LPFG) using Mach-Zehnder and Sagnac interference of the optical path. The LPFG sensor achieved a good repeatability and stability of temperature response with a sensitivity of 0.083nm/°C in the range of 40°C-120°C. Comparing to the traditional fiber Bragg grating (FBG) sensor, the LPFG sensor shows 10 times higher temperature sensitivity than that of the FBG, so the problem of low sensitivity of FBG is solved. Otherwise, Mach-Zehnder and Sagnac interference of the optical path have the advantage of simple structure and good practicability which can replace the complex optical path in the special environment.
Magnetic resonance imaging (MRI) is a revolutionary tool in medical imaging, which plays an important role in clinical diagnosis. Compressive sensing (CS) has shown great potential in significantly reducing the acquisition time of MRI scanning. However, how to improve the reconstruction quality with limited k-space data is still a challenge. MRI images are featured with large area of smooth regions, sharp edges and rich textures. Motivated by these facts, we propose a nonlocal autoregressive model (NAM) for CS MRI reconstruction. Nonlocal similarity between image patches is exploited as a regularization term to constrain the nonlocal feature in MRI images, which is very helpful in preserving edge sharpness. While an autoregressive regularization term is employed to describe the linear correlation between neighboring pixels, which preserves more spatial details. Different from previous work, we reconstruct an MRI image patch utilizing correlations both among patches and among neighboring pixels. Extensive experimental results demonstrate that our method outperforms mainstream methods in MRI reconstruction in terms of both subjective quality and objective quality.
Block truncation coding (BTC) is a fast image compression technique applied in spatial domain. Traditional BTC and its variants mainly focus on reducing computational complexity for low bit rate compression, at the cost of lower quality of decoded images, especially for images with rich texture. To solve this problem, in this paper, a quadtree-based block truncation coding algorithm combined with adaptive bit plane transmission is proposed. First, the direction of edge in each block is detected using Sobel operator. For the block with minimal size, adaptive bit plane is utilized to optimize the BTC, which depends on its MSE loss encoded by absolute moment block truncation coding (AMBTC). Extensive experimental results show that our method gains 0.85 dB PSNR on average compare to some other state-of-the-art BTC variants. So it is desirable for real time image compression applications.
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