SCFA-Net: Fast and High-Quality Network for Large-Size DR Image Enhancement

Digital radiography (DR) technology is characterized by high spatial resolution, substantial information capacity, and a wide dynamic range, making it widely applicable in the field of industrial nondestructive testing. However, in practical applications, the diverse types of workpieces often lead to many components with uneven thickness and structural anomalies. This results in challenges such as overexposure in thinner areas and underexposure in larger regions during DR inspections. In addition, as detector technology has advanced, the pixel matrices of detectors have continually increased, with common models now reaching 4k $\times 4$ k, capable of producing large-sized DR images. Nevertheless, existing methods often struggle to efficiently process or even infer 4k images. To successfully infer 4k images while maintaining the quality of enhanced results, we propose a scale-aware channel Fourier transform attention network (SCFA-Net) based on semantic alignment. Semantic-aligned scale-aware module (SAM) is designed to adapt to the feature extraction requirements of large-sized images. Furthermore, we innovatively introduce an attention mechanism to capture the long-range global dependencies inherent in 4k images. The proposed Fourier transform module addresses the issue of exposure inconsistency in DR efficiently. Moreover, to further achieve high-quality inference of large DR images, we ultimately modified the inference network by replacing local information aggregation (LRG) with global information aggregation. Results indicate that our method can process 4k resolution DR images at a speed of 48 frames/s, achieving state-of-the-art (SOTA) performance on two public datasets and a DR dataset.