This chapter discusses modern techniques for image improvement, including pixel editing, clarity enhancement, and minimal-size object recognition. An outline of photo enhancement and how deep learning could address its issues comes first. Both sophisticated techniques like cut-out and style transfer and frequently used ones like rotation and scaling are covered in this chapter. Additionally included are techniques for manipulating pixels, such as brightness adjustment, colour space conversion, and denoising algorithms. Assisting clarity issues like super-resolution, deblurring, and contrast amplification are also covered in this chapter. In order to address the issues with minimal-size object recognition, the chapter also looks into single-shot detectors and multi-scale networks. Through case studies and applications in medical imaging, autonomous driving, and surveillance systems, the value of these techniques is demonstrated. A discussion of prospective future study areas and how these techniques could affect computer vision and image processing brings the chapter to a close.
TopIntroduction
Image enhancement techniques enhance digital images' visual quality and interpretability by improving attributes like contrast, brightness, sharpness, and color. They are used in fields like medical imaging, remote sensing, surveillance, and computer vision. Deep learning has revolutionized image enhancement techniques, particularly convolutional neural networks (CNNs), by learning complex patterns and features from image data. This leads to data-driven approaches that automatically learn and adapt image enhancement functions based on large-scale training data. These techniques offer advantages over traditional methods, such as capturing intricate image characteristics, adapting to varying conditions, and providing superior performance in challenging scenarios (Lv et al., 2021). This chapter explores advanced image enhancement techniques like augmentation, pixel manipulation, clarity enhancement, and minimum size object recognition using deep learning approaches, discussing their underlying concepts, methodologies, and practical implementations. By incorporating deep learning and advanced techniques, we can overcome traditional limitations and unlock new possibilities for improving digital image quality and utility (Fujita et al., 2019).
Image enhancement techniques are essential in fields like medical imaging, remote sensing, Earth Observation, and surveillance systems. They improve the quality and interpretability of digital images, addressing limitations in raw or unprocessed images. These techniques improve diagnostic accuracy, patient care, and treatment planning. They also aid in remote sensing, monitoring natural resources, disaster management, and urban planning. In surveillance systems, image enhancement techniques improve object recognition, identification, and license plate reading, aiding forensic analysis and security measures (Illarionova et al., 2021).
Image enhancement techniques are essential in various fields, including computer vision, multimedia, forensics, and law enforcement. They improve accuracy, robustness, and visual quality in various applications, such as object detection, recognition, and tracking. These techniques also enhance digital content and entertainment quality, identifying faces, enhancing details, and extracting valuable evidence. Despite facing challenges and limitations, image enhancement techniques enable accurate analysis, decision-making, and understanding of underlying information within digital images (Jose et al., 2021).
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Digital images face noise and artifacts, affecting image quality and enhancement techniques. Removing noise while preserving important details is a challenging task.
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Image enhancement is subjective due to varying perceptions and lack of ground truth, making it challenging to objectively evaluate and compare different methods.
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Identifying the right balance between over-enhancement and under-enhancement is crucial for enhancing images effectively. Over-enhancement can introduce artifacts, exaggerate noise, and distort features, while under-enhancement may not provide enough improvement.
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Image-specific challenges arise in various image types, such as low-light visibility and limited details, and high-contrast regions. Developing effective enhancement techniques is a complex task.
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Advanced image enhancement techniques, like deep learning, can be computationally intensive, requiring significant resources and time, making deployment in real-time or resource-constrained environments challenging.
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Image enhancement algorithms are trained on a specific dataset, which may not cover all potential images. This poses a challenge in generalizing techniques to diverse datasets and scenarios.
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Image enhancement techniques must balance enhancing desirable features like edges and textures with preserving original content. Excessive enhancement can cause loss of details or alter inherent characteristics. Deep learning-based methods lack interpretability and transparency, limiting adoption and trust in critical applications.