1. Introduction - radhikahorti/Embedding-Synthetic-Pattern GitHub Wiki
In computer vision, image processing, and related domains, embedding synthetic patterns onto pictures serves a variety of applications. Some examples may be: A dataset can be supplemented with synthetic patterns. Data augmentation is a typical strategy for increasing the quantity and variety of a training dataset, which can help machine learning models perform better. Fresh training examples that help the model generalize better by adding synthetic patterns to photos. Adding synthetic patterns to photos can be used to assess the resilience of image processing techniques or computer vision systems. These artificial patterns can be used to simulate real-world noise, distortions, or artifacts that the system may face in practice. Image processing or computer vision techniques must frequently be evaluated under controlled settings by researchers and engineers. They may evaluate how well these algorithms function in certain contexts by embedding synthetic patterns with known features. Synthetic patterns are useful for testing and troubleshooting. By providing predictable inputs, they can assist in identifying and resolving difficulties with image processing pipelines, object recognition, tracking, and other activities. Synthetic patterns can be used to watermark photos or movies in the context of digital media. Watermarks, which can be visible or invisible, are frequently used to protect intellectual property, authenticate material, and manage digital rights.
Synthetic patterns can be used to produce targets or markers that computer vision systems can easily identify. This is often employed in robotics, augmented reality, and computer vision applications that need the tracking of objects or markers. Synthetic patterns are employed in specific industries, such as computer graphics and virtual reality, to replicate natural textures, lighting conditions, or environmental impacts in order to create realistic and immersive virtual worlds. QR codes and barcodes are examples of synthetic patterns that are used for security and authentication. These patterns may include encoded information that machines can simply read and analyze.
In summary, embedding synthetic patterns onto images is a versatile technique with various applications in image processing, computer vision, data augmentation, testing, security, and more. These patterns are valuable tools for improving, evaluating, and extending the capabilities of image-based systems and technologies.
Histogram Comparison - Compare histograms of the two images. Histogram-based methods like the Bhattacharyya distance or the Earth Mover’s Distance (EMD) can be used to measure image similarity.
The choice of method depends on the specific use case and what aspect of the images you want to compare (e.g., content, quality, structure, or objects). Some methods are more suitable for certain tasks, such as object detection, while others are more appropriate for measuring overall image similarity. The right approach may also involve a combination of these methods, depending on the complexity of the comparison task.
1.1 Motivation
In the fascinating realm of image processing, the integration of synthetic patterns into raw images stands as a captivating endeavor that merges creativity with technological innovation. This report delves into the intricacies of embedding synthetic patterns into raw images, exploring the potential to elevate visual aesthetics and unlock new dimensions of artistic expression. As we navigate through the technical nuances of this process, we uncover the transformative power it holds in augmenting the inherent beauty of raw images. Beyond the mere fusion of pixels, this exploration seeks to unravel the profound impact on visual storytelling, offering a glimpse into the future where the synthesis of reality and imagination converges seamlessly. This journey promises to unravel not only the technical advancements but also the artistic breakthroughs that lie at the intersection of raw imagery and synthetic ingenuity. Embarking on this report is an invitation to discover the boundless possibilities that arise when the artistry of human imagination intertwines with the precision of cutting-edge technology.
1.2 Objectives
Objectives of the provided problem statement are as follows: • Capture RAW Images. • To generate synthetic pattern using image to image stable diffusion • To embed the synthetic patterns into raw images • Construct a local statistics comparison block. • Develop a software tool for the same.
1.3 Literature survey
Embedding synthetic patterns into raw images explores various techniques and methodologies employed by researchers in the field of image processing and computer vision. Researchers have extensively investigated the integration of synthetic patterns into raw images for applications such as image watermarking, data hiding, and augmented reality.
Classic methods, such as spatial domain techniques and frequency domain transformations, have been employed for embedding synthetic patterns, with studies focusing on robustness, imperceptibility, and capacity. Depending on the task’s unique purpose and needs, there are numerous ways for embedding synthetic patterns onto photographs. Here are some typical methods: Image Editing Software: - To manually apply synthetic patterns to images, use image editing software such as Adobe Photoshop, GIMP, or other graphic design tools. This approach is appropriate for activities that need artistic or creative design, such as adding logos, typography, or watermarks. Image Processing Libraries:
- Use image processing libraries and tools, such as OpenCV, Python’s Pillow, or MATLAB, to programmatically add synthetic patterns to pictures. This enables automation and exact control over the embedding process. Data Augmentation: In Python, you may use libraries like Augmentor or imgaug to enhance data in machine learning and computer vision applications. These libraries include routines for applying different modifications, such as adding synthetic patterns, to a picture collection. Image Generation Models: Train or employ generative models such as Generative Adversarial Networks (GANs) or Variational Autoencoders (VAEs) to create synthetic patterns that may be superimposed on photographs. GANs, for example, may be taught to produce realistic textures or objects that can be superimposed on existing photos. At the core of stable diffusion methods is the concept of a diffusion process. The process starts with an initial noisy observation (e.g., an image with added noise). Over a series of steps or time intervals, the data is gradually refined, with noise being reduced and structure emerging. This process is meant to mimic the way data evolves or ”diffuses” over time. Additionally, recent advancements in deep learning approaches, including generative models and neural network-based methods, have shown promising results in seamlessly integrating synthetic patterns into raw images.
The survey also delves into the challenges associated with this task, such as maintaining image quality, resisting attacks, and ensuring compatibility with diverse image formats. Stable diffusion methods, often referred to as ”stochastic stable diffusion” or ”diffusion models,” are a class of generative models used in machine learning and deep learning. These models are designed for various tasks, including image generation, data denoising, and image super-resolution. They are based on the concept of simulating the diffusion process of data using a series of stochastic transformations.Notable examples of stable diffusion models include the ”Image GPT” model and the ”Denoising Diffusion Probabilistic Models (DDPM)”. By analyzing the existing literature, the report aims to provide a comprehensive understanding of the current state-of-the-art techniques and trends in embedding synthetic patterns into raw images, paving the way for further advancements in this dynamic and evolving field.
The job of comparing two pictures is prevalent in many domains, including computer vision, image processing, and pattern detection. The purpose of picture comparison might range from recognizing similarities or dissimilarities to determining the extent to which two photos differ. Here are several strategies and methods for comparing two images:
Pixel-by-Pixel Analysis Comparing photos pixel by pixel is one of the most basic approaches. You may determine the absolute difference by subtracting the matching pixel values in one image from the other. The overall difference score reveals how different the two photos are.
The Structural Similarity Index (SSI) measures how similar two structures are. The SSIM metric is a popular way to compare two photographs. It evaluates brightness, contrast, and structure and assigns a score ranging from -1 to 1.
1.4 Problem statement
Develop software tool to embed synthetic patterns into raw images while preserving natural image statistics.