Image Enhancement: Techniques to Improve Picture Quality

The goal of image enhancement is to improve image quality by increasing contrast, removing blur and noise, and correcting geometric distortions. Additionally, image restoration is a technique that estimates the original image by assuming known models of blur or noise. Both techniques play vital roles in image processing applications and can be implemented using Python libraries like OpenCV or MATLAB's Image Processing Toolbox with functions such as imfilter and wiener2 for restoration.

Image enhancement methods are broadly classified into frequency domain and spatial domain approaches. Frequency domain methods treat the image as a two-dimensional signal and perform signal enhancement using 2D Fourier transform. For instance, low-pass filtering (e.g., Gaussian blur via cv2.GaussianBlur()) allows only low-frequency components to pass, effectively removing noise. High-pass filtering (e.g., using Laplacian operators with cv2.Laplacian()) enhances high-frequency signals like edges, making blurry images sharper. Spatial domain algorithms, such as local averaging (implemented with kernel convolution) and median filtering (replacing each pixel with the median value in its neighborhood using cv2.medianBlur()), are commonly used to reduce or eliminate noise.

In summary, image enhancement and restoration are critical techniques in image processing that improve visual quality and provide a better data foundation for subsequent analysis and applications, such as computer vision tasks using TensorFlow or PyTorch frameworks.