Capturing high-quality photos in an underwater atmosphere is complicated, as light attenuation, color distortion, and reduced contrast pose significant challenges. However, one fact usually ignored is the non-uniform texture degradation in distorted images. The loss of comprehensive textures in underwater images poses obstacles in object detection and recognition. To address this problem, we have introduced an image enhancement model called scene adaptive color compensation and multi-weight fusion for extracting fine textural details under diverse environments and enhancing the overall quality of the underwater imagery. Our method blends three input images derived from the adaptive color-compensating and color-corrected version of the degraded image. The first two input images are used to adjust the low contrast and dehazing of the image respectively. Similarly, the third input image is used to extract the fine texture details based on different scales and orientations of the image. Finally, the input images with their associated weight maps are normalized and fused through multi-weight fusion. The proposed model is tested on a distinct set of underwater imagery with varying levels of degradation and frequently outperformed state-of-the-art methods, producing significant improvements in texture visibility, reducing color distortion, and enhancing the overall quality of the submerged images.
Graph-based multi-view clustering can effectively reveal the latent cluster structure of multi-view data, however, it remains challenging to construct high-quality graphs by exploring the discriminant information from data. Most of the existing graph-based methods usually construct the graph in the original space or subspace, which may not take the differences of semantic information in different spaces into considerations. Moreover, existing approaches generally measure the relevance of the pair-wise views, which may result in insufficient information exploitation and limited graph representation. To remedy these issues, we proposed an innovative multi-view clustering algorithm called reciprocal consistency graph learning (RCGL), which learns consistency representation by aligning the semantic information from three spaces simultaneously to uncover latent complementarity and consistency among different views. Specifically, we construct the low-dimensional space based on linear projection to preserve essential semantic information. Furthermore, RCGL maps the low-dimension representation from linear space into nonlinear space via Hilbert–Schmidt independence criterion, to explore the nonlinear information hidden in data. Moreover, multiple graphs in kernel space are extended to tensor space for learning a high-order graph, which is helpful to better measure the correlation between multiple views. Finally, the objective function is solved by an alternating optimization scheme. Extensive experiments on five benchmark multi-view datasets demonstrate that RCGL outperforms state-of-the-art baselines markedly.
Underwater image generally suffers from color degradation and poor visibility due to the attenuation and scattering of the propagated light. To restore the real environment of the underwater, most of previous methods only consider the scattering in the polarimetric image recovery model. To compensate for the absorption in different wavelengths, a simple but effective underwater image enhancement method that based on the adaptive polarimetric image recovery model is proposed in this paper. In order to achieve automatic estimation of model parameters, the backscattered light estimation is based on the quad-tree decomposition to extract the fully backscattered area automatically. The experimental results demonstrate that the visual quality of underwater images are efficiently enhanced.
Very low bit rate coding is an important part of the MPEG-4 standard. To obtain maximum compression in videoconference sequences a technique called model-based coding can be used. With this technique, the face of the speaker is represented by a model, and its movement between frames is coded with a small set of parameters. As with
all MPEG standards, the encoder algorithm is not described and this leaves the door open to many possible implementations. In this paper, a summary of model-based coding is presented. Many fields have been studied, including 3D wire-frame models, face detection, facial feature extraction, motion parameter estimation, and face model
parameter coding. Several problems need to be solved before model-based video communication will become practical. Some of them are discussed in this paper, such as how to detect and locate a face in an image, how to extract parameters from an image to adapt a face model, and how to compress the animation parameters.
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