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Image restoration techniques aim to reverse the effects of degradation and restore the image as closely as possible to its original or desired state. The process involves analysing the image and applying algorithms and filters to remove or reduce the degradations. The ultimate goal is to enhance the
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Image restoration can be broadly categorized into two main types: spatial domain and frequency domain methods. Spatial domain techniques operate directly on the image pixels, while frequency domain methods transform the image into the frequency domain using techniques such as the
Fourier transform,
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and camera mis-focus. Image restoration is performed by reversing the process that blurred the image and such is performed by imaging a point source and use the point source image, which is called the Point Spread
Function (PSF) to restore the image information lost to the blurring process.
404:. Convolutional neural networks (CNNs) have shown promising results in various image restoration tasks, including denoising, super-resolution, and inpainting. The use of generative adversarial networks (GANs) has also gained attention for realistic image restoration.
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The objective of image restoration techniques is to reduce noise and recover resolution loss. Image processing techniques are performed either in the image domain or the frequency domain. The most straightforward and a conventional technique for image restoration is
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of both the image and the PSF and undo the resolution loss caused by the blurring factors. Nowadays, photo restoration is done using digital tools and software to fix any type of damage images may have and improve the general quality and definition of the details.
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Despite significant advancements in image restoration, several challenges remain. Some of the key challenges include handling complex degradations, dealing with limited information, and addressing the trade-off between restoration quality and computation time.
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in that the latter is designed to emphasize features of the image that make the image more pleasing to the observer, but not necessarily to produce realistic data from a scientific point of view. Image enhancement techniques (like
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Image restoration techniques are commonly used in digital photography to correct imperfections caused by factors like motion blur, lens aberrations, and sensor noise. They can also be used to restore old and damaged photographs.
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plays a significant role in preserving historical documents, artworks, and photographs. By reducing noise, enhancing faded details, and removing artifacts, valuable visual content can be preserved for future generations.
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This technique aims to recover the original image by estimating the inverse of the degradation function. However, it is highly sensitive to noise and can amplify noise in the restoration process.
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Additionally, emerging technologies such as computational photography and multi-sensor imaging are expected to provide new avenues for image restoration research and applications.
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techniques involve transforming the image from the spatial domain to the frequency domain, typically using the
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This technique minimizes the total variation of an image while preserving important image details. It is effective in removing noise while maintaining image edges.
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With image enhancement noise can effectively be removed by sacrificing some resolution, but this is not acceptable in many applications. In a
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where restoration operations are performed. Both approaches have their advantages and are suitable for different types of image degradation.
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By incorporating constraints on the solution, this method reduces noise and restores the image while preserving important image details.
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is the operation of taking a corrupt/noisy image and estimating the clean, original image. Corruption may come in many forms such as
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This technique replaces each pixel value with the median value in its local neighborhood, effectively reducing impulse noise.
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Spatial domain techniques primarily operate on the pixel values of an image. Some common methods in this domain include:
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or de-blurring by a nearest neighbor procedure) provided by imaging packages use no
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Image restoration has a wide range of applications in various fields, including:
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Image restoration is crucial in medical imaging to improve the accuracy of
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The future of image restoration is likely to be driven by developments in
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It is used for enhancing images that suffer from both additive and
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200:Learn how and when to remove this message
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49:Relevant discussion may be found on the
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648:3D reconstruction from multiple images
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453:"Digital Signal Processing | Journal"
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53:. Please help Knowledge (XXG) by
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