In the recent period, significant progress has been achieved towards reconstructing the 3D face model from face image. With the support of the render engines and sufficient data, the reconstruction results are fine in detail. Nevertheless, the research on the 3D face reconstruction with texture from a single unrestricted face image is imperfect. The rebuild process lacks essential structure and texture information in the profile and the craniofacial region. To address this problem, we present a method of creating a 3D full face-and-head model with photorealistic texture from a single “in-the-wild” face image in this paper. To this end, we introduce a pipeline to integrate the highly-detailed face model into the basic model. Specifically, the basic model was built by multilinear optimization, and the highly-detailed face model which represents the facial features generated by constrained illumination distribution. Additionally, to infer the invisible region texture information corresponding to the input face image, we design an effective architecture with the generative adversarial network (GAN) for panoramic UV texture generation. The final results after UV texture mapping were visualized in the experiment, which demonstrates that the model faithfully recovers the photorealistic details in arbitrary perspective. Furthermore, compared to the state-of-the-art facial modeling techniques and existing commercial solutions, our method takes less input and performs better in surface detail.

Synthetic aperture radar (SAR) has been extensively applied in remote sensing applications. Nevertheless, it is a challenge to process and interpret SAR images. The key to interpreting SAR images lies in transforming them into other forms of remote sensing images to extract valuable hidden remote sensing information. Currently, the conversion of SAR images to optical images produces low-quality results and incomplete spectral information. To address these problems, an end-to-end network model, S2MS-GAN, is proposed for converting SAR images into multispectral images. In this process, to tackle the issues of noise and image generation quality, a TV-BM3D module is introduced into the generator model. Through TV regularization, block-matching, and 3D filtering, these two modules can preserve the edges and reduce the speckle noise in SAR images. In addition, spectral attention is added to improve the spectral features of the generated MS images. Furthermore, we construct a very high-resolution SAR-to-MS image dataset, S2MS-HR, with a spatial resolution of 0.3 m, which is currently the most comprehensive dataset available for high-resolution SAR-to-MS image interpretation. Finally, a series of experiments are conducted on the relevant dataset. Both quantitative and qualitative evaluations demonstrate that our method outperforms several state-of-the-art models in translation performance. The solution effectively facilitates high-quality transitions of SAR images across different types.

Color image encryption is challenging due to large data volumes, strong inter-pixel correlations, and high redundancy, limiting the use of conventional text-oriented encryption. While chaos-based methods have been studied extensively, many schemes struggle with unjustified key spaces, plaintext-dependent keystreams, insufficient evaluation of keystream randomness, and inadequate support for DNA rule design. This work proposes a secure color image cryptosystem by integrating a fractional-order chaotic pseudo-random number generator (FCPRNG) with a DNA-level encryption framework. The proposed FCPRNG is built on a three-dimensional fractional-order Lü system under a non-uniform-grid numerical scheme, and its admissible parameter–order joint space is systematically identified to restrict keystream generation to regions of sustained bounded chaos. Based on this generator, a plaintext-independent encryption framework is constructed, decoupling keystream generation from plaintext processing. In addition, a purely DNA-level encryption architecture is designed, in which the plain image undergoes joint RGB-channel permutation and pixel-value transformation through an inter-layer pixel-value-dependent DNA encoding mechanism, followed by DNA-domain confusion and diffusion operations. Experimental results show statistically suitable keystreams, strong ciphertext randomness, high sensitivity to the key and plaintext, and moderate robustness against noise and cropping. These results indicate that the scheme provides a structurally improved and empirically effective approach to secure color image encryption.

3D face reconstruction from a single image still suffers from low accuracy and inability to recover textures in invisible regions. In this paper, we propose a method for generating a 3D portrait with complete texture. The coarse face-and-head model and texture parameters are obtained using 3D Morphable Model fitting. We design an image-geometric inverse renderer that acquires normal, albedo, and light to jointly reconstruct the facial details. Then, we use a texture fusion network to extract the valid texture from rendered faces employing different viewpoints. Specifically, this fused texture recovers the invisible region of the input face, which illustrates the realistic surface of our 3D geometric model. Our approach faithfully reconstructs the original face details, including the profiles and the head region. Extensive experiments are performed to demonstrate that our method outperforms state-of-the-art techniques in various challenging scenarios.