Deep learning-based RGB-thermal image denoising: review and applications
Abstract: Recently, vision-based detection (VD) technology has been well-developed, and its general-purpose object detection algorithms have been applied in various scenes. VD can be divided into two categories based on the type of modality: single-modal (single RGB or single thermal) and bimodal. Image denoising is typically the first stage of image processing in VD, where redundant information and noisy data are removed to produce clearer images for effective object detection. This study reviews deep learning-based image denoising for RGB and thermal images, investigating the denoising procedure, methodologies, and performances of algorithms tested with benchmark datasets. After introducing denoising models, the main results on public RGB and thermal datasets are presented and analyzed, and conclusions of objective comparison in practical effect are drawn. This review can serve as a reference for researchers in RGB–infrared denoising, image restoration, and related fields.
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