Learning to Downsample for Segmentation of Ultra-High Resolution Images

by   Chen Jin, et al.

Segmentation of ultra-high resolution images with deep learning is challenging because of their enormous size, often millions or even billions of pixels. Typical solutions drastically downsample the image uniformly to meet memory constraints, implicitly assuming all pixels equally important by sampling at the same density at all spatial locations. However this assumption is not true and compromises the performance of deep learning techniques that have proved powerful on standard-sized images. For example with uniform downsampling, see green boxed region in Fig.1, the rider and bike do not have enough corresponding samples while the trees and buildings are oversampled, and lead to a negative effect on the segmentation prediction from the low-resolution downsampled image. In this work we show that learning the spatially varying downsampling strategy jointly with segmentation offers advantages in segmenting large images with limited computational budget. Fig.1 shows that our method adapts the sampling density over different locations so that more samples are collected from the small important regions and less from the others, which in turn leads to better segmentation accuracy. We show on two public and one local high-resolution datasets that our method consistently learns sampling locations preserving more information and boosting segmentation accuracy over baseline methods.


page 1

page 3

page 6

page 8

page 13

page 14

page 17


Foveation for Segmentation of Ultra-High Resolution Images

Segmentation of ultra-high resolution images is challenging because of t...

Collaborative Global-Local Networks for Memory-Efficient Segmentation of Ultra-High Resolution Images

Segmentation of ultra-high resolution images is increasingly demanded, y...

SuperMask: Generating High-resolution object masks from multi-view, unaligned low-resolution MRIs

Three-dimensional segmentation in magnetic resonance images (MRI), which...

SuperVessel: Segmenting High-resolution Vessel from Low-resolution Retinal Image

Vascular segmentation extracts blood vessels from images and serves as t...

Generating Superpixels for High-resolution Images with Decoupled Patch Calibration

Superpixel segmentation has recently seen important progress benefiting ...

Automatic Segmentation of Fluorescence Lifetime Microscopy Images of Cells Using Multi-Resolution Community Detection

We have developed an automatic method for segmenting fluorescence lifeti...

Intra-Batch Supervision for Panoptic Segmentation on High-Resolution Images

Unified panoptic segmentation methods are achieving state-of-the-art res...

Please sign up or login with your details

Forgot password? Click here to reset