Locally-Focused Face Representation for Sketch-to-Image Generation Using Noise-Induced Refinement

• URL: http://arxiv.org/abs/2411.19005v1
• Date: Thu, 28 Nov 2024 09:12:56 GMT
• Title: Locally-Focused Face Representation for Sketch-to-Image Generation Using Noise-Induced Refinement
• Authors: Muhammad Umer Ramzan, Ali Zia, Abdelwahed Khamis, yman Elgharabawy, Ahmad Liaqat, Usman Ali,
• Abstract summary: This paper presents a novel deep-learning framework that significantly enhances the transformation of rudimentary face sketches into high-fidelity colour images. Our approach effectively captures and enhances critical facial features through a block attention mechanism within an encoder-decoder architecture. The model sets a new state-of-the-art in sketch-to-image generation, can generalize across sketch types, and offers a robust solution for applications such as criminal identification in law enforcement.
• Score: 1.7409266903306055
• License:
• Abstract: This paper presents a novel deep-learning framework that significantly enhances the transformation of rudimentary face sketches into high-fidelity colour images. Employing a Convolutional Block Attention-based Auto-encoder Network (CA2N), our approach effectively captures and enhances critical facial features through a block attention mechanism within an encoder-decoder architecture. Subsequently, the framework utilises a noise-induced conditional Generative Adversarial Network (cGAN) process that allows the system to maintain high performance even on domains unseen during the training. These enhancements lead to considerable improvements in image realism and fidelity, with our model achieving superior performance metrics that outperform the best method by FID margin of 17, 23, and 38 on CelebAMask-HQ, CUHK, and CUFSF datasets; respectively. The model sets a new state-of-the-art in sketch-to-image generation, can generalize across sketch types, and offers a robust solution for applications such as criminal identification in law enforcement.

Related papers

• Visual Autoregressive Modeling for Image Super-Resolution [14.935662351654601]
  We propose a novel visual autoregressive modeling for ISR framework with the form of next-scale prediction. We collect large-scale data and design a training process to obtain robust generative priors.
  arXiv  Detail & Related papers  (2025-01-31T09:53:47Z)
• $R^2$-Mesh: Reinforcement Learning Powered Mesh Reconstruction via Geometry and Appearance Refinement [5.810659946867557]
  Mesh reconstruction based on Neural Radiance Fields (NeRF) is popular in a variety of applications such as computer graphics, virtual reality, and medical imaging. We propose a novel algorithm that progressively generates and optimize meshes from multi-view images. Our method delivers highly competitive and robust performance in both mesh rendering quality and geometric quality.
  arXiv  Detail & Related papers  (2024-08-19T16:33:17Z)
• Fiducial Focus Augmentation for Facial Landmark Detection [4.433764381081446]
  We propose a novel image augmentation technique to enhance the model's understanding of facial structures. We employ a Siamese architecture-based training mechanism with a Deep Canonical Correlation Analysis (DCCA)-based loss. Our approach outperforms multiple state-of-the-art approaches across various benchmark datasets.
  arXiv  Detail & Related papers  (2024-02-23T01:34:00Z)
• A Comprehensive End-to-End Computer Vision Framework for Restoration and Recognition of Low-Quality Engineering Drawings [19.375278164300987]
  This paper focuses on restoring and recognizing low-quality engineering drawings. An end-to-end framework is proposed to improve the quality of the drawings and identify the graphical symbols on them. Experiments on real-world electrical diagrams show that the proposed framework achieves an accuracy of 98.98% and a recall of 99.33%.
  arXiv  Detail & Related papers  (2023-12-21T07:22:25Z)
• DGNet: Dynamic Gradient-Guided Network for Water-Related Optics Image Enhancement [77.0360085530701]
  Underwater image enhancement (UIE) is a challenging task due to the complex degradation caused by underwater environments. Previous methods often idealize the degradation process, and neglect the impact of medium noise and object motion on the distribution of image features. Our approach utilizes predicted images to dynamically update pseudo-labels, adding a dynamic gradient to optimize the network's gradient space.
  arXiv  Detail & Related papers  (2023-12-12T06:07:21Z)
• Distance Weighted Trans Network for Image Completion [52.318730994423106]
  We propose a new architecture that relies on Distance-based Weighted Transformer (DWT) to better understand the relationships between an image's components. CNNs are used to augment the local texture information of coarse priors. DWT blocks are used to recover certain coarse textures and coherent visual structures.
  arXiv  Detail & Related papers  (2023-10-11T12:46:11Z)
• CM-GAN: Image Inpainting with Cascaded Modulation GAN and Object-Aware Training [112.96224800952724]
  We propose cascaded modulation GAN (CM-GAN) to generate plausible image structures when dealing with large holes in complex images. In each decoder block, global modulation is first applied to perform coarse semantic-aware synthesis structure, then spatial modulation is applied on the output of global modulation to further adjust the feature map in a spatially adaptive fashion. In addition, we design an object-aware training scheme to prevent the network from hallucinating new objects inside holes, fulfilling the needs of object removal tasks in real-world scenarios.
  arXiv  Detail & Related papers  (2022-03-22T16:13:27Z)
• A Generic Approach for Enhancing GANs by Regularized Latent Optimization [79.00740660219256]
  We introduce a generic framework called em generative-model inference that is capable of enhancing pre-trained GANs effectively and seamlessly. Our basic idea is to efficiently infer the optimal latent distribution for the given requirements using Wasserstein gradient flow techniques.
  arXiv  Detail & Related papers  (2021-12-07T05:22:50Z)
• Asymmetric CNN for image super-resolution [102.96131810686231]
  Deep convolutional neural networks (CNNs) have been widely applied for low-level vision over the past five years. We propose an asymmetric CNN (ACNet) comprising an asymmetric block (AB), a mem?ory enhancement block (MEB) and a high-frequency feature enhancement block (HFFEB) for image super-resolution. Our ACNet can effectively address single image super-resolution (SISR), blind SISR and blind SISR of blind noise problems.
  arXiv  Detail & Related papers  (2021-03-25T07:10:46Z)
• Towards Coding for Human and Machine Vision: A Scalable Image Coding Approach [104.02201472370801]
  We come up with a novel image coding framework by leveraging both the compressive and the generative models. By introducing advanced generative models, we train a flexible network to reconstruct images from compact feature representations and the reference pixels. Experimental results demonstrate the superiority of our framework in both human visual quality and facial landmark detection.
  arXiv  Detail & Related papers  (2020-01-09T10:37:17Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.