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Acoustic-resolution photoacoustic microscopy (AR-PAM) suffers from degraded lateral resolution due to acoustic diffraction. Here, a resolution enhancement strategy for AR-PAM via a mean-reverting diffusion model was proposed to achieve the transition from acoustic resolution to optical resolution. By modeling the degradation process from high-resolution image to low-resolution AR-PAM image with stable Gaussian noise (i.e., mean state), a mean-reverting diffusion model is trained to learn prior information of the data distribution. Then the learned prior is employed to generate a high-resolution image from the AR-PAM image by iteratively sampling the noisy state. The performance of the proposed method was validated utilizing the simulated and in vivo experimental data under varying lateral resolutions and noise levels. The results show that an over 3.6-fold enhancement in lateral resolution was achieved. The image quality can be effectively improved, with a notable enhancement of ∼66% in PSNR and ∼480% in SSIM for in vivo data.

In this paper, we propose an adaptive interpolation scheme based on iterative back-projection and human visual system based quality metric for image sequences. Initial estimates of each up-sampled image can be generated individually by using subpixel interpolation and subpixel motion estimation in the spatial and temporal domains respectively. Then, based on the initial estimates and edge information, up-sampled images are derived by using an iterative back-projection technique and a quality metric based on the human visual system. After fusing the up-sampled images into a final version, a low-pass filter is applied as a post-processing step to reduce the effect of blocking artifacts in each reconstructed up-sampled image. Our experimental results demonstrate that, in terms of PSNR (Peak Signal-to-Noise Ratio) and NQM (Noise Quality Metric), the proposed scheme outperforms four existing methods.

In this paper, a novel edge-oriented neural-network-based adaptive interpolation scheme for natural image is proposed. An image analysis module is used to classify pixels of the input image into non-oriented class and oriented class. The bilinear interpolation is used to interpolate the non-oriented regions and a neural network is proposed to interpolate the oriented regions. High-resolution digital images along with supervised learning algorithms can be used to automatically train the proposed neural network. Simulation results demonstrate that the proposed new resolution enhancement algorithm can produce higher visual quality of the interpolated image than the conventional interpolation methods.