Abstract
For scanning electron microscopes with high resolution and a strong electric field, biomass materials under observation are prone to radiation damage from the electron beam. This results in blurred or non-viable images, which affect further observation of material microscopic morphology and characterization. Restoring blurred images to their original sharpness is still a challenging problem in image processing. Traditional methods can't effectively separate image context dependency and texture information, affect the effect of image enhancement and deblurring, and are prone to gradient disappearance during model training, resulting in great difficulty in model training. In this paper, we propose the use of an improved U-Net (U-shaped Convolutional Neural Network) to achieve image enhancement for biomass material characterization and restore blurred images to their original sharpness. The main work is as follows: use of depthwise separable convolution instead of standard convolution in U-Net to reduce model computation effort and parameters; embedding wavelet transform into the U-Net structure to separate image context and texture information, thereby improving image reconstruction quality; using dense multi-receptive field channel modules to extract image detail information, thereby better transmitting the image features and network gradients, and reduce the difficulty of training. The experiments show that the improved U-Net model proposed in this paper is suitable and effective for enhanced deblurring of biomass material characterization images. The PSNR (Peak Signal-to-noise Ratio) and SSIM (Structural Similarity) are enhanced as well.
Highlights
In recent years, a looming energy crisis and greater emphasis on environmental protection have led to 100% environmentally-friendly biomass composites becoming a popular research topic
In this work, we propose an improved U-Net for sharpening biomass material characterization images
We propose an improved U-Net [23,24,25] model for biomass material characterization image enhancement and the model structure is shown in Fig. 1, which mainly consists of depthwise separable convolution [26], residual depthwise separable convolution [27], wavelet transform, a dense multiple receiver domain channel module (DMRFC) and a contextual channel attention module (CCA)
Summary
A looming energy crisis and greater emphasis on environmental protection have led to 100% environmentally-friendly biomass composites becoming a popular research topic. The rapid development of scanning electron microscopy imaging is a common and effective analytical technique used for surface morphology observation. It has high resolution, high magnification, strong depth of field, a large field of view, stereoscopic imaging and can directly observe microstructures on uneven surfaces of various specimens [2]. The issue of unclear material characterization images due to irradiation damage or breakdown of the SEM electric field affecting biomass samples is a common phenomenon when researching on the characterization of natural fiber composites
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