Non-uniform deblurring in HDR image reconstruction

Image restoration for ring-array photoacoustic tomography based on an attention mechanism driven conditional generative adversarial network

Ring-Array photoacoustic tomography (PAT) systems have shown great promise in non-invasive biomedical imaging. However, images produced by these systems often suffer from quality degradation due to non-ideal imaging conditions, with common issues including blurring and streak artifacts. To address these challenges, we propose an image restoration method based on a conditional generative adversarial network (CGAN) framework. Our approach integrates a hybrid spatial and channel attention mechanism within a Residual Shifted Window Transformer Module (RSTM) to enhance the generator's performance. Additionally, we have developed a comprehensive loss function to balance pixel-level accuracy, detail preservation, and perceptual quality. We further incorporate a gamma correction module to enhance the contrast of the network's output. Experimental results on both simulated and in vivo data demonstrate that our method significantly improves resolution and restores overall image quality.

Image restoration for ring-array photoacoustic tomography system based on blind spatially rotational deconvolution

Ring-array photoacoustic tomography (PAT) system has been widely used in noninvasive biomedical imaging. However, the reconstructed image usually suffers from spatially rotational blur and streak artifacts due to the non-ideal imaging conditions. To improve the reconstructed image towards higher quality, we propose a concept of spatially rotational convolution to formulate the image blur process, then we build a regularized restoration problem model accordingly and design an alternating minimization algorithm which is called blind spatially rotational deconvolution to achieve the restored image. Besides, we also present an image preprocessing method based on the proposed algorithm to remove the streak artifacts. We take experiments on phantoms and in vivo biological tissues for evaluation, the results show that our approach can significantly enhance the resolution of the image obtained from ring-array PAT system and remove the streak artifacts effectively.

A Contribution Algorithm from LDRI to HDRI

High dynamic range image (HDRI) which is combined with low dynamic range image (LDRI) needs to be mapped to a low dynamic area to display. In the process of mapping, it is impossible to determine the contribution of low dynamic image sequences in the display images, so that it results in a problem that the low dynamic images cannot be accurately selected. In this paper, for the first time, a contribution algorithm from LDRI to HDRI according to the corresponding response curve of the camera is proposed.

Recognizing blurred, nonfrontal, illumination, and expression variant partially occluded faces

The focus of this paper is on the problem of recognizing faces across space-varying motion blur, changes in pose, illumination, and expression, as well as partial occlusion, when only a single image per subject is available in the gallery. We show how the blur, incurred due to relative motion between the camera and the subject during exposure, can be estimated from the alpha matte of pixels that straddle the boundary between the face and the background. We also devise a strategy to automatically generate the trimap required for matte estimation. Having computed the motion via the matte of the probe, we account for pose variations by synthesizing from the intensity image of the frontal gallery a face image that matches the pose of the probe. To handle illumination, expression variations, and partial occlusion, we model the probe as a linear combination of nine blurred illumination basis images in the synthesized nonfrontal pose, plus a sparse occlusion. We also advocate a recognition metric that capitalizes on the sparsity of the occluded pixels. The performance of our method is extensively validated on synthetic as well as real face data.