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Recent studies have explored the generation of three-dimensional (3D) meshes from single images. A key challenge in this area is the difficulty of improving both the generalization and detail simultaneously in 3D mesh generation. To address this issue, existing methods utilize fixed-resolution mesh features to train networks for generalization. This approach is capable of generating the overall 3D shape without limitations on object categories. However, the generated shape often exhibits a blurred surface and suffers from suboptimal texture resolution due to the fixed-resolution mesh features. In this study, we propose a joint optimization method that enhances geometry and texture by integrating generalized 3D mesh generation with adjustable mesh resolution. Specifically, we apply an inverse-rendering-based remeshing technique that enables the estimation of complex-shaped mesh estimations without relying on fixed-resolution structures. After remeshing, we enhance the texture to improve the detailed quality of the remeshed mesh via a texture enhancement diffusion model. By separating the tasks of generalization, detailed geometry estimation, and texture enhancement and adapting different target features for each specific network, the proposed joint optimization method effectively addresses the characteristics of individual objects, resulting in increased surface detail and the generation of high-quality textures. Experimental results on the Google Scanned Objects and ShapeNet datasets demonstrate that the proposed method significantly improves the accuracy of 3D geometry and texture estimation, as evaluated by the PSNR, SSIM, LPIPS, and CD metrics.

Synthetic aperture radar (SAR) is an important tool for observing the oceanic internal wave phenomenon. However, owing to the unstable imaging quality of SAR on oceanic internal waves, the texture details of internal wave images are usually unclear, which is not conducive to the subsequent applications of the images. To cope with this problem, a texture enhancement method for oceanic internal wave SAR images is proposed in this paper, which is based on non-local mean (NLM) filtering and texture layer enhancement (TLE). Since the strong speckle noise commonly present in internal wave images is simultaneously enhanced during texture enhancement, resulting in degraded image quality, NLM filtering is first performed to suppress speckle noise. Then, the denoised image is decomposed into the structure layer and the texture layer, and a texture layer enhancement method oriented to the texture characteristics of oceanic internal waves is proposed and applied. Finally, the enhanced texture layer and the structure layer are combined to reconstruct the final enhanced image. Experiments are conducted based on the Gaofen-3 real SAR data, and the results demonstrate that the proposed method performs well in suppressing speckle noise, maintaining overall image brightness, and enhancing internal wave texture details.

This paper proposes a texture-enhanced network (TENet) for intertidal sediment and habitat classification using multiband multipolarization synthetic aperture radar (SAR) images. The architecture introduces the texture enhancement module (TEM) into the UNet framework to explicitly learn global texture information from SAR images. The study sites are chosen from the northern part of the intertidal zones in the German Wadden Sea. Results show that the presented TENet model is able to detail the intertidal surface types, including land, seagrass, bivalves, bright sands/beach, water, sediments, and thin coverage of vegetation or bivalves. To further assess its performance, we quantitatively compared our results from the TENet model with different instance segmentation models for the same areas of interest. The TENet model gives finer classification accuracies and shows great potential in providing more precise locations.

We present TED-Face, a new method for recovering high-fidelity 3D facial geometry and appearance with enhanced textures from single-view images. While vision-based face reconstruction has received intensive research in the past decades due to its broad applications, it remains a challenging problem because human eyes are particularly sensitive to numerically minute yet perceptually significant details. Previous methods that seek to minimize reconstruction errors within a low-dimensional face space can suffer from this issue and generate close yet low-fidelity approximations. The loss of high-frequency texture details is a key factor in their process, which we propose to address by learning to recover both dense radiance residuals and sparse facial texture features from a single image, in addition to the variables solved by previous work—shape, appearance, illumination, and camera. We integrate the estimation of all these factors in a single unified deep neural network and train it on several popular face reconstruction datasets. We also introduce two new metrics, visual fidelity (VIF) and structural similarity (SSIM), to compensate for the fact that reconstruction error is not a consistent perceptual metric of quality. On the popular FaceWarehouse facial reconstruction benchmark, our proposed system achieves a VIF score of 0.4802 and an SSIM score of 0.9622, improving over the state-of-the-art Deep3D method by 6.69% and 0.86%, respectively. On the widely used LS3D-300W dataset, we obtain a VIF score of 0.3922 and an SSIM score of 0.9079 for indoor images, and the scores for outdoor images are 0.4100 and 0.9160, respectively, which also represent an improvement over those of Deep3D. These results show that our method is able to recover visually more realistic facial appearance details compared with previous methods.

To achieve the non-destructive detection of peanut defects, a multi-target identification method based on the multispectral system and improved Faster RCNN is proposed in this paper. In terms of the system, the root-mean-square contrast method was employed to select the characteristic wavelengths for defects, such as mildew spots, mechanical damage, and the germ of peanuts. Then, a multispectral light source system based on a symmetric integrating sphere was designed with 2% nonuniformity illumination. In terms of Faster RCNN improvement, a texture-based attention and a feature enhancement module were designed to enhance the performance of its backbone. In the experiments, a peanut-deficient multispectral dataset with 1300 sets was collected to verify the detection performance. The results show that the evaluation metrics of all improved compared with the original network, especially in the VGG16 backbone network, where the mean average precision (mAP) reached 99.97%. In addition, the ablation experiments also verify the effectiveness of the proposed texture module and texture enhancement module in peanut defects detection. In conclusion, texture imaging enhancement and efficient extraction are effective methods to improve the network performance for multi-target peanut defect detection.

There are many potential hazard sources along high-speed railways that threaten the safety of railway operation. Traditional ground search methods are failing to meet the needs of safe and efficient investigation. In order to accurately and efficiently locate hazard sources along the high-speed railway, this paper proposes a texture-enhanced ResUNet (TE-ResUNet) model for railway hazard sources extraction from high-resolution remote sensing images. According to the characteristics of hazard sources in remote sensing images, TE-ResUNet adopts texture enhancement modules to enhance the texture details of low-level features, and thus improve the extraction accuracy of boundaries and small targets. In addition, a multi-scale Lovász loss function is proposed to deal with the class imbalance problem and force the texture enhancement modules to learn better parameters. The proposed method is compared with the existing methods, namely, FCN8s, PSPNet, DeepLabv3, and AEUNet. The experimental results on the GF-2 railway hazard source dataset show that the TE-ResUNet is superior in terms of overall accuracy, F1-score, and recall. This indicates that the proposed TE-ResUNet can achieve accurate and effective hazard sources extraction, while ensuring high recall for small-area targets.