HVDualformer: Histogram-Vision Dual Transformer for White Balance

Abstract

Capturing images under different color temperatures can result in color casts, causing the color presented in photos to differ from what is perceived by the human eye. Correcting these color temperature shifts to achieve White Balance (WB) is a challenging task, requiring the identification of variations in color tones from diverse light sources and the removal of color casts. The advent of deep neural networks has significantly advanced the progress of WB methods, evolving from simply identifying the scene illumination color to directly producing a color-corrected image from the color-shifted input. To better map color distributions and scene information from the input to the WB image, we propose HVDualformer, an end-to-end histogram-vision dual transformer architecture that can rectify color temperature features from WB color histograms and exploit them to adjust image features to yield accurate WB results. Extensive experimental results on public benchmark datasets demonstrate that the proposed model performs favorably against state-of-the-art methods.