What is the best architecture for Auto-Encoder for image reconstruction?
I don't know about an architecture being definitively the best, but there are some best practices you can follow. Check out these papers:

Learning to Generate Images with Perceptual Similarity Metrics
Push it to the Limit: Discover Edge-Cases in Image Data with Autoencoders
Walking the Tightrope: An Investigation of the Convolutional Autoencoder Bottleneck

To sum it up, residual blocks in between downsampling, SSIM as a loss function, and larger feature map sizes in the bottleneck seem to improve reconstruction quality significantly. How that translates to the latent space is not entirely clear yet.