Object recognition is conducted using red, green, and blue (RGB) images in object recognition studies. However, RGB images in low-light environments or environments where other objects occlude the target objects cause poor object recognition performance. In contrast, infrared (IR) images provide acceptable object recognition performance in these environments because they detect IR waves rather than visible illumination. In this paper, we propose an inter- and intra-weighted cross-fusion network (Infusion-Net), which improves object recognition performance by combining the strengths of the RGB-IR image pairs. Infusion-Net connects dual object detection models using a high-frequency (HF) assistant (HFA) to combine the advantages of RGB-IR images. To extract HF components, the HFA transforms input images into a discrete cosine transform domain. The extracted HF components are weighted via pretrained inter- and intra-weights for feature-domain cross-fusion. The inter-weighted fused features are transmitted to each other’s networks to complement the limitations of each modality. The intra-weighted features are also used to enhance any insufficient HF components of the target objects. Thus, the experimental results present the superiority of the proposed network and present improved performance of the multispectral object recognition task.

Among various developments in the field of computer vision, single image super-resolution of images is one of the most essential tasks. However, compared to the integer magnification model for super-resolution, research on arbitrary magnification has been overlooked. In addition, the importance of single image super-resolution at arbitrary magnification is emphasized for tasks such as object recognition and satellite image magnification. In this study, we propose a model that performs arbitrary magnification while retaining the advantages of integer magnification. The proposed model extends the integer magnification image to the target magnification in the discrete cosine transform (DCT) spectral domain. The broadening of the DCT spectral domain results in a lack of high-frequency components. To solve this problem, we propose a high-frequency attention network for arbitrary magnification so that high-frequency information can be restored. In addition, only high-frequency components are extracted from the image with a mask generated by a hyperparameter in the DCT domain. Therefore, the high-frequency components that have a substantial impact on image quality are recovered by this procedure. The proposed framework achieves the performance of an integer magnification and correctly retrieves the high-frequency components lost between the arbitrary magnifications. We experimentally validated our model’s superiority over state-of-the-art models.

Scene text detection and recognition, such as automatic license plate recognition, is a technology utilized in various applications. Although numerous studies have been conducted to improve recognition accuracy, accuracy decreases when low-quality legacy license plate images are input into a recognition module due to low image quality and a lack of resolution. To obtain better recognition accuracy, this study proposes a high-frequency augmented license plate recognition model in which the super-resolution module and the license plate recognition module are integrated and trained collaboratively via a proposed gradual end-to-end learning-based optimization. To optimally train our model, we propose a holistic feature extraction method that effectively prevents generating grid patterns from the super-resolved image during the training process. Moreover, to exploit high-frequency information that affects the performance of license plate recognition, we propose a license plate recognition module based on high-frequency augmentation. Furthermore, we propose a gradual end-to-end learning process based on weight freezing with three steps. Our three-step methodological approach can properly optimize each module to provide robust recognition performance. The experimental results show that our model is superior to existing approaches in low-quality legacy conditions on UFPR and Greek vehicle datasets.