A deep learning based method with the convolutional neural network (CNN) algorithm for determining the impact parameters is developed by using constrained molecular-dynamics model simulations, focusing on the heavy-ion collisions at the low-intermediate incident energies from several tens to one hundred MeV/nucleon in which the emissions of heavy fragments with the charge numbers larger than three become crucial. To make the CNN applicable in the task of the impact-parameter determination at the present energy range, specific improvements are made in the input selection, the CNN construction, and the CNN training. It is demonstrated from the comparisons of the deep CNN method and the conventional methods with the impact-parameter-sensitive observables that the deep CNN method shows better performance for determining the impact parameters, especially leading to the capability of providing better recognition of the central collision events. With a proper consideration of the experimental filter effect in both training and testing processes to keep consistency with the actual experiments, the good performance of the deep CNN method holds and performs significantly better in terms of predicting the impact parameters and recognizing the central collision events compared with conventional methods, demonstrating the superiority of the present deep CNN method. The deep CNN method with the consideration of the filter effect is applied in the deduction of nuclear stopping power. Higher accuracy for the stopping power deduction is achieved benefitting from the better impact-parameter determination using the deep CNN method, compared with using conventional methods. This result reveals the importance of selecting a reliable impact-parameter-determination method in the experimental deduction of the nuclear stopping power as well as other observables.

Color image binarization plays a pivotal role in image preprocessing work and significantly impacts subsequent tasks, particularly for text recognition. This paper concentrates on document image binarization (DIB), which aims to separate an image into a foreground (text) and background (non-text content). We thoroughly analyze conventional and deep-learning-based approaches and conclude that prevailing DIB methods leverage deep learning technology. Furthermore, we explore the receptive fields of pre- and post-network training to underscore the Transformer model’s advantages. Subsequently, we introduce a lightweight model based on the U-Net structure and enhanced with the MobileViT module to capture global information features in document images better. Given its adeptness at learning both local and global features, our proposed model demonstrates competitive performance on two standard datasets (DIBCO2012 and DIBCO2017) and good robustness on the DIBCO2019 dataset. Notably, our proposed method presents a straightforward end-to-end model devoid of additional image preprocessing or post-processing, eschewing the use of ensemble models. Moreover, its parameter count is less than one-eighth of the model, which achieves the best results on most DIBCO datasets. Finally, two sets of ablation experiments are conducted to verify the effectiveness of the proposed binarization model.

Image decolorization is used to realize the color-to-gray conversion, which is widely used in black-and-white printing, digital-ink display, photo stylization, etc. Image contrast fidelity preservation is the key issue during this conversion process. We present a parametric model combining image entropy and the Canny edge retention ratio (CERR). Maximizing the image entropy keeps the global image contrast content, while maximizing the CERR enhances the local image contrast, mainly along the edges. The weighting parameter λ can be robustly estimated. Experimental results on Cadik’s and the COLOR250 datasets demonstrate that the model can achieve significant performance improvement over state-of-the-art methods both qualitatively and quantitatively.

Color-to-grayscale conversion is a fundamental preprocessing task with widespread applications in digital printing, electronic ink displays, medical imaging, and artistic photo stylization. A primary challenge in this domain is to simultaneously preserve global luminance distribution and local contrast. To address this, we propose an adaptive conversion method centered on a novel objective function that integrates information entropy with Edge Content (EC), a metric for local gradient information. The key advantage of our approach is its ability to generate grayscale results that maintain both rich overall contrast and fine-grained local details. Compared with previous adaptive linear methods, our approach demonstrates superior qualitative and quantitative performance. Furthermore, by eliminating the need for computationally expensive edge detection, the proposed algorithm provides an effective solution to the color-to-grayscale conversion.

Color-to-gray image conversion is commonly used in applications such as printing, e-ink display, image stylization. Effective decolorization methods aim to maintain the naturalness and the image contrast after the conversion. We propose a method for this conversion problem using the image’s salient colors and radial basis functions. There are three steps in the proposed method. First, we quantize the image colors to a small number of salient representative colors, which essentially provides an adaptive mechanism for choosing the dominant image color regime that can be mapped to the grayscale with the maximum possible contrast structure. A carefully designed criterion is developed for choosing the appropriate number of quantization points in the k-means algorithm based on the elbow method. Second, two methods are proposed for ordering the quantized colors on the grayscale intensities, using the guideline that the ordering should agree with the human perceptual comfort when looking at common grayscale images. Finally, radial basis functions are used to extend the mapping from the quantized colors to the grayscale intensities to all colors of the original image’s pixels in a continuous fashion. Experimental results on the three benchmark datasets of color-to-gray conversion show significant performance improvement over state-of-the-art methods both qualitatively and quantitatively.

Color image binarization plays a pivotal role in image preprocessing work, significantly impacting subsequent tasks, particularly in text recognition. This paper concentrates on Document Image Binarization (DIB), aiming to separate a image into foreground (text) and background (non-text content). Through a thorough analysis of conventional and deep learning-based approaches, we conclude that prevailing DIB methods leverage deep learning technology. Furthermore, we explore the receptive fields pre- and post-network training to underscore the Transformer model's advantages. Subsequently, we introduce a lightweight model based on the U-Net structure, enhanced with the Mobile ViT module to better capture global information features in document images. Given its adeptness at learning both local and global features, our proposed model exhibits superior performance on two standard datasets (DIBCO2012 and DIBCO2017) compared to state-of-the-art methods. Notably, our proposed DIB method presents a straightforward end-to-end model devoid of additional image preprocessing or post-processing. Moreover, its parameter count is less than a quarter of the HIP'23 model, which achives best results on three datasets(DIBCO2012, DIBCO2017 and DIBCO2018). Finally, two sets of ablation experiments were conducted to verify the effectiveness of the proposed binarization model.