Learning‐based image mapping for degraded documents on E‐paper display

Abstract With the widespread use of E‐paper technology, numerous documents are being digitized and displayed on E‐paper screens. However, the display quality of degraded document images on E‐paper often suffers from a lack of detail. To address this challenge, we introduce a mapping model that converts color images into E‐paper display images. This model leverages U‐Net++ as its backbone, integrating residual connectivity and dual attention modules. Given the presence of varying stroke thicknesses in document images, a fixed‐size convolutional kernel is insufficient. Therefore, we propose multi‐branch channels and spatial attention modules (MCSAM), which combines the selective kernel network (SKNet) with a spatial attention mechanism to adaptively select the appropriate convolutional kernel size based on font size. To demonstrate its effectiveness, we tested the mapped images on a custom E‐paper display platform. Experimental results highlight the superior performance of our proposed method.