All-in-Focus Image Reconstruction Through AutoEncoder Methods

Abstract

Focal stacking is a technique that allows us to create images with a large depth of field, where everything in the scene is sharp and clear. However, creating such images is not easy, as it requires taking multiple pictures at different focus settings and then blending them together. In this paper, we present a novel approach to blending a focal stack using a special type of autoencoder, which is a neural network that can learn to compress and reconstruct data. Our autoencoder consists of several parts, each of which processes one input image and passes its information to the final part, which fuses them into one output image. Unlike other methods, our approach is capable of inpainting and denoising resulting in sharp, clean all-in-focus images. Our approach does not require any prior training or a large dataset, which makes it fast and effective. We evaluate our method on various kinds of images and compare it with other widely used methods. We demonstrate that our method can produce superior focal stacked images with higher accuracy and quality. This paper reveals a new and promising way of using a neural network to aid in microphotography, microscopy, and visual computing, by enhancing the quality of focal stacked images.

Date of Award	Jul 2023
Original language	English (US)
Awarding Institution	Computer, Electrical and Mathematical Sciences and Engineering
Supervisor	Peter Wonka (Supervisor)