MATLAB Code Implementation of Kinoform

Kinoform is an image processing method based on digital holography technology that can record and reconstruct both phase and amplitude information of objects. Below we present the fundamental approach for MATLAB implementation of kinoforms and demonstrate how to generate three key results: the original image, kinoform, and reconstructed image. ### 1. Original Image Preparation First, prepare an input image as the target object. Typically, grayscale images are used to better observe phase and amplitude variations. MATLAB provides various methods for reading and preprocessing images, such as resizing and normalization, to ensure data compatibility with subsequent Fourier transform computations. Key functions include `imread()` for image loading and `im2double()` for data normalization to [0,1] range. ### 2. Kinoform Generation The kinoform generation process primarily utilizes Fourier transform or Fresnel diffraction calculations. In MATLAB, the `fft2()` function (2D Fast Fourier Transform) performs frequency domain computation on the input image. By manipulating phase information combined with amplitude data, we can construct the complex amplitude distribution of the kinoform. Additionally, to enhance reconstruction quality, random phase modulation or appropriate filtering techniques are typically implemented using `rand()` for phase randomization and filter design functions for noise reduction. ### 3. Reconstruction Calculation The reconstruction process involves inverse transformation of the kinoform, employing inverse Fourier transform or inverse Fresnel diffraction calculations. MATLAB's `ifft2()` (Inverse Fast Fourier Transform) converts the kinoform's frequency domain data back to spatial domain images. The reconstruction results typically display approximate images of the original object, though some distortion may occur due to numerical precision limitations and potential noise interference. Phase unwrapping algorithms may be applied to improve reconstruction fidelity. ### 4. Result Visualization Finally, MATLAB's `subplot()` functionality can display the original image, kinoform, and reconstructed image in a single window for comparative analysis. Kinoforms typically appear as characteristic images with interference patterns, while reconstructed images aim to restore the original object's contours and details. The `imagesc()` or `imshow()` functions are commonly used for optimal visualization of both amplitude and phase components. Through these steps, we can implement kinoform generation and reconstruction using MATLAB, providing a viable simulation method for research in optical information processing and digital holography applications. The implementation demonstrates practical applications of Fourier optics principles in computational imaging.