How to denoise and restore an image in computer vision
Denoising and restoration of an image are important phases in an image processing pipeline. Image processing is a set of techniques that involve manipulating and analyzing digital images to enhance their quality, extract information, or perform specific tasks.
In image processing, images are typically represented as two-dimensional arrays of pixels, where each pixel contains color or intensity information. Preprocessing an image involves applying various techniques to enhance the image quality and make it suitable for further analysis. One crucial step in image preprocessing is image denoising and restoration.
Image restoration
Image restoration refers to the process of improving the quality and appearance of a degraded or damaged image. It involves recovering lost or degraded information to restore the image to its original or desired state.
Let's visualize image restoration with an image.
Image restoration techniques can be classified into two main categories:
Frequency domain (transform-based).
Spatial domain restoration (filtering based).
Frequency domain
Frequency domain restoration techniques are image restoration methods that operate in the frequency domain. They involve transforming the image from the spatial domain to the frequency domain using mathematical transformations like the Fourier Transform or Wavelet Transform.
Deconvolution: It is used to recover sharpness and detail in blurred images. It involves estimating and compensating for the blurring effects by deconvolving the degraded image with an estimated blur kernel in the frequency domain, resulting in the restoration of high-frequency details.
Super-resolution: This technique uses frequency domain operations, such as interpolation or extrapolation, to predict or reconstruct high-frequency components in the frequency domain to enable the generation of a higher-resolution image with improved visual quality and finer details.
Spatial domain
Spatial domain is an image restoration method that operates directly on the pixel values of an image, meaning it manipulates the pixel values within the image itself. It includes inpainting and denoising.
Inpainting: It is a technique used to fill in missing or damaged regions of an image with plausible content. It involves estimating the missing information based on the surrounding pixels or structures.
Now let's discuss denoising in image restoration in detail.
Denoising an Image
Before moving on to denoising techniques, let's understand what is meant by noise in an image.
What is noise in an image?
Noise in an image refers to unwanted and random variations in pixel values that deviate from the true content of the image. It can be seen as a disturbance that disrupts the desired information in the image and degrades the quality of an image.
Some common sources of noise include sensor noise and image processing noise in digital images. Sensor noise arises from the image sensor itself during the image capture process from factors such as thermal noise, which results in random fluctuations and variations in pixel values. Image processing noise can occur during various stages, including analog-to-digital conversion and transmission over networks.
Denoising techniques
Image denoising techniques can be classified based on the domains in which they operate. The two primary domains used for denoising are the spatial domain and the transform domain. Below is a classification of denoising filters based on these domains.
Spatial domain
Spatial domain filters, also known as direct or pixel-based filters, operate directly on the pixel values of an image. These filters consider the values of neighboring pixels to estimate the denoised value for each pixel. They are further classified into linear and nonlinear filters:
Linear spatial filters
Gaussian filter: It applies a weighted average to each pixel based on a Gaussian kernel. It reduces noise in an image by smoothing the pixel values with the amount of smoothing controlled by the standard deviation of the Gaussian kernel.
Mean filter: It is a simple and widely used filter for reducing noise by smoothing the image. The mean filter replaces each pixel with the average value of its neighboring pixels.
Nonlinear spatial filters
Median filter: The median filter replaces the value of each pixel with the median value of the pixel intensities within a defined neighborhood.
Bilateral filter: It applies a weighted average to each pixel by taking into account the differences in spatial distance and pixel intensity, which results in noise reduction while maintaining important image features and sharp edges.
Transform domain
Transform domain denoising techniques, such as wavelet denoising, contourlet denoising, and curvelet denoising, apply specific transforms to effectively reduce noise in images. Here's a brief explanation of each technique:
Wavelet denoising: It applies the wavelet transform to decompose an image into different frequency bands as the wavelet coefficients corresponding to noise are comparatively of higher magnitude. Therefore, it thresholds the wavelet coefficients and suppresses the noise while retaining the significant image features.
Contourlet denoising: It is similar to wavelet denoising and utilizes the contourlet transform to decompose an image into subbands at multiple scales and orientations. It preserves image structure along contours and edges.
Curvelet denoising: It is designed to handle images with curve-like structures and utilizes the curvelet transform to decompose an image into curvelet coefficients that provide high directional sensitivity.
Note: A practical tip is to explore a combination of denoising or restoration methods and compare their results to achieve the best outcome but avoid over-aggressive denoising.
Conclusion
In conclusion, some challenges may arise from finding the right trade-off between noise reduction and preserving important features and dealing with complex or unknown noise characteristics. Overcoming these challenges requires careful analysis, customization, and exploration of combinations of techniques to achieve optimal results in denoising and restoration tasks.
