Wholeimage

Wholeimage is a term used in digital imaging and computer vision to describe processing or analysis performed on the entire image as a single unit, rather than on cropped patches or localized regions. The concept contrasts with patch-based or region-based approaches, where information is extracted from smaller portions of the image and then combined.

In practice, wholeimage processing is common in tasks such as image classification, where a model ingests a full-resolution image to predict a label, and in some segmentation or dense prediction tasks that aim to maintain full-image context during inference. It often requires architectural strategies to handle variable image sizes and high memory demands, such as fully convolutional networks, global pooling, multi-scale processing, or input downsampling.

Advantages of wholeimage methods include the preservation of global context, reduced boundary artifacts, and simpler end-to-end training pipelines. These approaches can leverage information across the entire scene, potentially improving performance on tasks where context is important and avoiding seams or inconsistencies that patch-based methods may introduce.

Challenges include substantial memory and computational cost, difficulty handling very high-resolution imagery, and sensitivity to variations in illumination or aspect ratio. Effective use of wholeimage models often relies on careful data preprocessing, normalization, and augmentation to ensure robustness across diverse inputs.

The term is used across computer vision literature and industry to describe end-to-end approaches that process whole images. While some tasks and datasets employ wholeimage inputs as a baseline or default configuration, patch-based methods remain relevant when memory constraints or precise localization are primary concerns.